The current study uses a support vector machine to implement a Point to Point Critical Path Monitoring (P2PCPM) based Denial of Service (DOS) Attack detection technique
for Vehicular Communication Network (VCN) resource management
An Investigation into the Effectiveness of Machine Learning Techniques for In...Oyeniyi Samuel
The document investigates the effectiveness of machine learning techniques for intrusion detection. It evaluates six machine learning algorithms (Naive Bayes, Multi-Layer Perceptron Neural Networks, Support Vector Machine, Random Forests, Logistic Model Tree Induction, and Decision Tree) on the NSL-KDDTrain+ dataset. The experimental results show that the Logistic Model Tree Induction method performs best with a classification accuracy of 99.40%, F-measure of 0.991, and lowest false positive rate of 0.32%.
Secure intrusion detection and attack measure selectionUvaraj Shan
This document proposes NICE, a framework for secure intrusion detection and attack mitigation in virtual network systems. NICE uses distributed agents on cloud servers to monitor traffic, detect vulnerabilities, and generate attack graphs. It profiles virtual machines to identify their state and vulnerabilities. When potential attacks are detected, NICE can quarantine suspicious VMs and inspect their traffic. The attack analyzer correlates alerts, constructs attack graphs, and selects appropriate countermeasures based on the graphs. Evaluations show NICE can effectively detect attacks while minimizing performance overhead for the cloud system.
Secure intrusion detection and attack measure selection in virtual network sy...Uvaraj Shan
This document proposes NICE, a framework for secure intrusion detection and attack mitigation in virtual network systems. NICE uses distributed agents on cloud servers to monitor traffic, detect vulnerabilities, and generate attack graphs. It profiles virtual machines to identify their state and vulnerabilities. When potential attacks are detected, NICE can quarantine suspicious VMs and inspect their traffic. The attack analyzer correlates alerts, constructs attack graphs, and selects appropriate countermeasures based on the graphs. Evaluations show NICE can effectively detect attacks while minimizing performance overhead for the cloud system.
Classification of Malware Attacks Using Machine Learning In Decision TreeCSCJournals
Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
This document summarizes research on using various data mining classification techniques to handle false alerts in intrusion detection systems. The researchers tested many data mining procedures on the KDD Cup 99 dataset, including multilayer perceptron neural networks, rule-based models, support vector machines, naive Bayes, and association rule mining. The best accuracy was 92% for multilayer perceptrons, but rule-based models had the fastest training time at 4 seconds. The researchers concluded that different techniques should be used together to handle different types of network attacks.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
Constructing a predictive model for an intelligent network intrusion detectionAlebachew Chiche
This document presents a study that constructs a predictive model for network intrusion detection using data mining techniques. The study uses the KDD Cup 99 intrusion detection dataset to build classification models using J48 decision tree, JRip rule induction, Naive Bayes, and multilayer perceptron algorithms. The J48 decision tree algorithm achieved the highest accuracy of 99.91% and was selected to build the predictive model. This model was then integrated with a knowledge-based system to build an intelligent network intrusion detection system capable of automatically detecting network attacks, mapping detections to attack categories, and updating the training data over time. Experimental evaluation found the integrated system achieved 91.43% accuracy and 83% user acceptance in detecting network intrusions
DDOS ATTACK DETECTION ON INTERNET OF THINGS USING UNSUPERVISED ALGORITHMSijfls
The increase in the deployment of IoT networks has improved productivity of humans and organisations.
However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent
weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS
attack in IoT networks by classifying incoming network packets on the transport layer as either
“Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep
learning algorithms and two clustering algorithms were independently trained for mitigating DDoS
attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and
UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during
the experimentation phase. The accuracy score and normalized-mutual-information score are used to
quantify the classification performance of the four algorithms. Our results show that the autoencoder
performed overall best with the highest accuracy across all the datasets.
An Investigation into the Effectiveness of Machine Learning Techniques for In...Oyeniyi Samuel
The document investigates the effectiveness of machine learning techniques for intrusion detection. It evaluates six machine learning algorithms (Naive Bayes, Multi-Layer Perceptron Neural Networks, Support Vector Machine, Random Forests, Logistic Model Tree Induction, and Decision Tree) on the NSL-KDDTrain+ dataset. The experimental results show that the Logistic Model Tree Induction method performs best with a classification accuracy of 99.40%, F-measure of 0.991, and lowest false positive rate of 0.32%.
Secure intrusion detection and attack measure selectionUvaraj Shan
This document proposes NICE, a framework for secure intrusion detection and attack mitigation in virtual network systems. NICE uses distributed agents on cloud servers to monitor traffic, detect vulnerabilities, and generate attack graphs. It profiles virtual machines to identify their state and vulnerabilities. When potential attacks are detected, NICE can quarantine suspicious VMs and inspect their traffic. The attack analyzer correlates alerts, constructs attack graphs, and selects appropriate countermeasures based on the graphs. Evaluations show NICE can effectively detect attacks while minimizing performance overhead for the cloud system.
Secure intrusion detection and attack measure selection in virtual network sy...Uvaraj Shan
This document proposes NICE, a framework for secure intrusion detection and attack mitigation in virtual network systems. NICE uses distributed agents on cloud servers to monitor traffic, detect vulnerabilities, and generate attack graphs. It profiles virtual machines to identify their state and vulnerabilities. When potential attacks are detected, NICE can quarantine suspicious VMs and inspect their traffic. The attack analyzer correlates alerts, constructs attack graphs, and selects appropriate countermeasures based on the graphs. Evaluations show NICE can effectively detect attacks while minimizing performance overhead for the cloud system.
Classification of Malware Attacks Using Machine Learning In Decision TreeCSCJournals
Predicting cyberattacks using machine learning has become imperative since cyberattacks have increased exponentially due to the stealthy and sophisticated nature of adversaries. To have situational awareness and achieve defence in depth, using machine learning for threat prediction has become a prerequisite for cyber threat intelligence gathering. Some approaches to mitigating malware attacks include the use of spam filters, firewalls, and IDS/IPS configurations to detect attacks. However, threat actors are deploying adversarial machine learning techniques to exploit vulnerabilities. This paper explores the viability of using machine learning methods to predict malware attacks and build a classifier to automatically detect and label an event as “Has Detection or No Detection”. The purpose is to predict the probability of malware penetration and the extent of manipulation on the network nodes for cyber threat intelligence. To demonstrate the applicability of our work, we use a decision tree (DT) algorithms to learn dataset for evaluation. The dataset was from Microsoft Malware threat prediction website Kaggle. We identify probably cyberattacks on smart grid, use attack scenarios to determine penetrations and manipulations. The results show that ML methods can be applied in smart grid cyber supply chain environment to detect cyberattacks and predict future trends.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
This document summarizes research on using various data mining classification techniques to handle false alerts in intrusion detection systems. The researchers tested many data mining procedures on the KDD Cup 99 dataset, including multilayer perceptron neural networks, rule-based models, support vector machines, naive Bayes, and association rule mining. The best accuracy was 92% for multilayer perceptrons, but rule-based models had the fastest training time at 4 seconds. The researchers concluded that different techniques should be used together to handle different types of network attacks.
CLASSIFICATION PROCEDURES FOR INTRUSION DETECTION BASED ON KDD CUP 99 DATA SETIJNSA Journal
In network security framework, intrusion detection is one of a benchmark part and is a fundamental way to protect PC from many threads. The huge issue in intrusion detection is presented as a huge number of false alerts; this issue motivates several experts to discover the solution for minifying false alerts according to data mining that is a consideration as analysis procedure utilized in a large data e.g. KDD CUP 99. This paper presented various data mining classification for handling false alerts in intrusion detection as reviewed. According to the result of testing many procedure of data mining on KDD CUP 99 that is no individual procedure can reveal all attack class, with high accuracy and without false alerts. The best accuracy in Multilayer Perceptron is 92%; however, the best Training Time in Rule based model is 4 seconds . It is concluded that ,various procedures should be utilized to handle several of network attacks.
Constructing a predictive model for an intelligent network intrusion detectionAlebachew Chiche
This document presents a study that constructs a predictive model for network intrusion detection using data mining techniques. The study uses the KDD Cup 99 intrusion detection dataset to build classification models using J48 decision tree, JRip rule induction, Naive Bayes, and multilayer perceptron algorithms. The J48 decision tree algorithm achieved the highest accuracy of 99.91% and was selected to build the predictive model. This model was then integrated with a knowledge-based system to build an intelligent network intrusion detection system capable of automatically detecting network attacks, mapping detections to attack categories, and updating the training data over time. Experimental evaluation found the integrated system achieved 91.43% accuracy and 83% user acceptance in detecting network intrusions
DDOS ATTACK DETECTION ON INTERNET OF THINGS USING UNSUPERVISED ALGORITHMSijfls
The increase in the deployment of IoT networks has improved productivity of humans and organisations.
However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent
weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS
attack in IoT networks by classifying incoming network packets on the transport layer as either
“Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep
learning algorithms and two clustering algorithms were independently trained for mitigating DDoS
attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and
UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during
the experimentation phase. The accuracy score and normalized-mutual-information score are used to
quantify the classification performance of the four algorithms. Our results show that the autoencoder
performed overall best with the highest accuracy across all the datasets.
DDoS Attack Detection on Internet o Things using Unsupervised Algorithmsijfls
The increase in the deployment of IoT networks has improved productivity of humans and organisations. However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS attack in IoT networks by classifying incoming network packets on the transport layer as either “Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep learning algorithms and two clustering algorithms were independently trained for mitigating DDoS attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during the experimentation phase. The accuracy score and normalized-mutual-information score are used to quantify the classification performance of the four algorithms. Our results show that the autoencoder performed overall best with the highest accuracy across all the datasets.
Secure intrusion detection and countermeasure selection in virtual system usi...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Detecting network attacks model based on a convolutional neural network IJECEIAES
Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
Machine learning-based intrusion detection system for detecting web attacksIAESIJAI
The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.
A new proactive feature selection model based on the enhanced optimization a...IJECEIAES
This document presents a new proactive feature selection (PFS) model to detect distributed reflection denial of service (DRDoS) attacks using an optimized feature selection approach. The PFS model uses swarm optimization and evolutionary algorithms like particle swarm optimization, bat algorithm, and differential evolution to select optimal features. It then evaluates selected features using machine learning classifiers like k-nearest neighbors, support vector machine, and random forest. The model was tested on the CICDDoS2019 dataset and achieved a high DRDoS detection accuracy of 89.59% while reducing the number of features. Evaluation metrics like accuracy, precision, recall and F1-score were also improved compared to previous models. The PFS model provides an effective approach
FLOODING ATTACKS DETECTION OF MOBILE AGENTS IN IP NETWORKScsandit
This document summarizes a research paper that proposes a new framework for detecting flooding attacks in mobile agent networks. The framework integrates divergence measures like Hellinger distance and Chi-square over a sketch data structure. The sketch data structure is used to derive probability distributions from traffic data in fixed memory. Divergence measures compare the current and prior probability distributions to detect deviations indicating attacks. The performance of detecting attacks while minimizing false alarms is evaluated using real network traces with injected flooding attacks. Experimental results show the proposed approach outperforms existing solutions.
System call frequency analysis-based generative adversarial network model for...IJECEIAES
In today's digital age, mobile applications have become essential in connecting people from diverse domains. They play a crucial role in enabling communication, facilitating business transactions, and providing access to a range of services. Mobile communication is widespread due to its portability and ease of use, with an increasing number of mobile devices projected to reach 18.22 billion by the end of 2025. However, this convenience comes at a cost, as cybercriminals are constantly looking for ways to exploit security vulnerabilities in mobile applications. Among the several varieties of malicious applications, zero-day malware is particularly dangerous since it cannot be removed by antivirus software. To detect zeroday Android malware, this paper introduces a novel approach based on generative adversarial networks (GANs), which generates new frequencies of feature vectors from system calls. In the proposed approach, the generator is fed with a mixture of real samples and noise, and then trained to create new samples, while the discriminator model aims to classify these samples as either real or fake. We assess the performance of our model through different measures, including loss functions, the Frechet Inception distance, and the inception score evaluation metrics.
The main goal of Intrusion Detection Systems (IDSs) is
to detect intrusions. This kind of detection system represents a
significant tool in traditional computer based systems for ensuring
cyber security. IDS model can be faster and reach more accurate
detection rates, by selecting the most related features from the
input dataset. Feature selection is an important stage of any IDs to
select the optimal subset of features that enhance the process of the
training model to become faster and reduce the complexity while
preserving or enhancing the performance of the system. In this
paper, we proposed a method that based on dividing the input
dataset into different subsets according to each attack. Then we
performed a feature selection technique using information gain
filter for each subset. Then the optimal features set is generated by
combining the list of features sets that obtained for each attack.
Experimental results that conducted on NSL-KDD dataset shows
that the proposed method for feature selection with fewer features,
make an improvement to the system accuracy while decreasing the
complexity. Moreover, a comparative study is performed to the
efficiency of technique for feature selection using different
classification methods. To enhance the overall performance,
another stage is conducted using Random Forest and PART on
voting learning algorithm. The results indicate that the best
accuracy is achieved when using the product probability rule.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
Network Intrusion Detection And Countermeasure Selection In Virtual Network (...ClaraZara1
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in detecting abnormal content in the traffic data during information passing from one node to another and also detects known attack signature and unknown attack. This approach is tested by running the artificial network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
This document provides an overview of a presentation titled "A Machine Learning Approach to Analyze Cloud Computing Attacks" given at the 5th International Conference on Contemporary Computing and Informatics. The presentation discusses introducing machine learning algorithms to detect various types of cloud computing attacks. It reviews previous work applying supervised, unsupervised, and reinforcement learning techniques for attack detection. The presentation concludes that machine learning provides an effective approach for cloud security but that more research is still needed, particularly for real-time attack detection and mitigation.
DDOS DETECTION IN SOFTWARE-DEFINED NETWORK (SDN) USING MACHINE LEARNINGIJCI JOURNAL
In recent years, the concept of cloud computing and the software-defined network (SDN) have spread
widely. The services provided by many sectors such as medicine, education, banking, and transportation
are being replaced gradually with cloud-based applications. Consequently, the availability of these
services is critical. However, the cloud infrastructure and services are vulnerable to attackers who aim to
breach its availability. One of the major threats to any system availability is a Denial-of-Service (DoS)
attack, which is intended to deny the legitimate user from accessing cloud resources. The Distributed
Denial-of-Service attack (DDoS) is a type of DoS attack which is considerably more effective and
dangerous. A lot of efforts have been made by the research community to detect DDoS attacks, however,
there is still a need for further efforts in this germane field. In this paper, machine learning techniques are
utilized to build a model that can detect DDoS attacks in Software-Defined Networks (SDN). The used ML
algorithms have shown high performance in the earliest studies; hence they have been used in this study
along with feature selection technique. Therefore, our model utilized these algorithms to detect DDoS
attacks in network traffic. The outcome of this experiment shows the impact of feature selection in
improving the model performance. Eventually, The Random Forest classifier has achieved the highest
accuracy of 0.99 in detecting DDoS attack.
USE OF MARKOV CHAIN FOR EARLY DETECTING DDOS ATTACKSIJNSA Journal
DDoS has a variety of types of mixed attacks. Botnet attackers can chain different types of DDoS attacks to confuse cybersecurity defenders. In this article, the attack type can be represented as the state of the model. Considering the attack type, we use this model to calculate the final attack probability. The final attack probability is then converted into one prediction vector, and the incoming attacks can be detected early before IDS issues an alert. The experiment results have shown that the prediction model that can make multi-vector DDoS detection and analysis easier.
Hybrid Technique for Detection of Denial of Service (DOS) Attack in Wireless ...Eswar Publications
Wireless Sensor Network (WSNs) are deployed at aggressive environments which are vulnerable to various security attacks such as Wormholes, Denial of Attacks and Sybil Attacks. There are various intrusion detection techniques that are used to identify attacks in a network with high accuracy level. This paper has focused on Denial of Service attack, since it is the most common attack that affects the environment severely. Therefore a new hybrid technique combining Hidden Markov Model with Ant Colony Optimization (HMM+ACO) has been
proposed that gives improved performance than the other techniques.
Visualize network anomaly detection by using k means clustering algorithmIJCNCJournal
With the ever increasing amount of new attacks in today’s world the amount of data will keep increasing,
and because of the base-rate fallacy the amount of false alarms will also increase. Another problem with
detection of attacks is that they usually isn’t detected until after the attack has taken place, this makes
defending against attacks hard and can easily lead to disclosure of sensitive information.
In this paper we choose K-means algorithm with the Kdd Cup 1999 network data set to evaluate the
performance of an unsupervised learning method for anomaly detection. The results of the evaluation
showed that a high detection rate can be achieve while maintaining a low false alarm rate .This paper
presents the result of using k-means clustering by applying Cluster 3.0 tool and visualized this result by
using TreeView visualization tool .
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document proposes a signature-based intrusion detection system using multithreading. It captures network packets and analyzes them for intrusions by comparing signatures to databases of known attacks. A multithreaded design is suggested to improve performance by processing packets in parallel threads. Agents would be deployed on the network with detection modules that use caching of frequent signatures to speed up analysis. An update module would transfer new frequent signatures to the caches.
Preemptive modelling towards classifying vulnerability of DDoS attack in SDN ...IJECEIAES
Software-Defined Networking (SDN) has become an essential networking concept towards escalating the networking capabilities that are highly demanded future internet system, which is immensely distributed in nature. Owing to the novel concept in the field of network, it is still shrouded with security problems. It is also found that the Distributed Denial-of-Service (DDoS) attack is one of the prominent problems in the SDN environment. After reviewing existing research solutions towards resisting DDoS attack in SDN, it is found that still there are many open-end issues. Therefore, these issues are identified and are addressed in this paper in the form of a preemptive model of security. Different from existing approaches, this model is capable of identifying any malicious activity that leads to a DDoS attack by performing a correct classification of attack strategy using a machine learning approach. The paper also discusses the applicability of best classifiers using machine learning that is effective against DDoS attack.
COPYRIGHTThis thesis is copyright materials protected under the .docxvoversbyobersby
COPYRIGHT
This thesis is copyright materials protected under the Berne Convection, the copyright Act 1999 and other international and national enactments in that behalf, on intellectual property. It may not be reproduced by any means in full or in part except for short extracts in fair dealing so for research or private study, critical scholarly review or discourse with acknowledgment, with written permission of the Dean School of Graduate Studies on behalf of both the author and XXX XXX University.ABSTRACT
With Fast growing internet world the risk of intrusion has also increased, as a result Intrusion Detection System (IDS) is the admired key research field. IDS are used to identify any suspicious activity or patterns in the network or machine, which endeavors the security features or compromise the machine. IDS majorly use all the features of the data. It is a keen observation that all the features are not of equal relevance for the detection of attacks. Moreover every feature does not contribute in enhancing the system performance significantly. The main aim of the work done is to develop an efficient denial of service network intrusion classification model. The specific objectives included: to analyse existing literature in intrusion detection systems; what are the techniques used to model IDS, types of network attacks, performance of various machine learning tools, how are network intrusion detection systems assessed; to find out top network traffic attributes that can be used to model denial of service intrusion detection; to develop a machine learning model for detection of denial of service network intrusion.Methods: The research design was experimental and data was collected by simulation using NSL-KDD dataset. By implementing Correlation Feature Selection (CFS) mechanism using three search algorithms, a smallest set of features is selected with all the features that are selected very frequently. Findings: The smallest subset of features chosen is the most nominal among all the feature subset found. Further, the performances using Artificial neural networks(ANN), decision trees, Support Vector Machines (SVM) and K-Nearest Neighbour (KNN) classifiers is compared for 7 subsets found by filter model and 41 attributes. Results: The outcome indicates a remarkable improvement in the performance metrics used for comparison of the two classifiers. The results show that using 17/18 selected features improves DOS types classification accuracies as compared to using the 41 features in the NSL-KDD dataset. It was further observed that using an ensemble of three classifiers with decision fusion performs better as compared to using a single classifier for DOS type’s classification. Among machine learning tools experimented, ANN achieved best classification accuracies followed by SVM and DT. KNN registered the lowest classification accuracies. Application: The proposed work with such an improved detection rate and lesser classification time and lar.
AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHMIJNSA Journal
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99
benchmark dataset and obtained reasonable detection rate.
NTRUSION D ETECTION S YSTEMS IN M OBILE A D H OC N ETWORKS : S TATE OF ...ijcsa
Mobile Ad Hoc Networks (MANETs) are more vulnerable
to different attacks. Prevention methods as
cryptographic techniques alone are not sufficient t
o make them secure; therefore, efficient intrusion
detection must be deployed and elaborated to facili
tate the identification of attacks. An Intrusion De
tection
System (IDS) aims to detect malicious and selfish n
odes in a network. The intrusion detection methods
used
normally for wired networks can no longer adequate
when adapted directly to a wireless ad-hoc network,
so existing techniques of intrusion detection have
to be changed and new techniques have to be determi
ned
to work efficiency and effectively in this new netw
ork architecture of MANETs. In this paper we give a
survey of different architectures and methods of in
trusion detection systems (IDSs) for MANETs
accordingly to the recent literature.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Ähnlich wie P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management
DDoS Attack Detection on Internet o Things using Unsupervised Algorithmsijfls
The increase in the deployment of IoT networks has improved productivity of humans and organisations. However, IoT networks are increasingly becoming platforms for launching DDoS attacks due to inherent weaker security and resource-constrained nature of IoT devices. This paper focusses on detecting DDoS attack in IoT networks by classifying incoming network packets on the transport layer as either “Suspicious” or “Benign” using unsupervised machine learning algorithms. In this work, two deep learning algorithms and two clustering algorithms were independently trained for mitigating DDoS attacks. We lay emphasis on exploitation based DDOS attacks which include TCP SYN-Flood attacks and UDP-Lag attacks. We use Mirai, BASHLITE and CICDDoS2019 dataset in training the algorithms during the experimentation phase. The accuracy score and normalized-mutual-information score are used to quantify the classification performance of the four algorithms. Our results show that the autoencoder performed overall best with the highest accuracy across all the datasets.
Secure intrusion detection and countermeasure selection in virtual system usi...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Detecting network attacks model based on a convolutional neural network IJECEIAES
Due to the increasing use of networks at present, Internet systems have raised many security problems, and statistics indicate that the rate of attacks or intrusions has increased excessively annually, and in the event of any malicious attack on network vulnerabilities or information systems, it may lead to serious disasters, violating policies on network security, i.e., “confidentiality, integrity, and availability” (CIA). Therefore, many detection systems, such as the intrusion detection system, appeared. In this paper, we built a system that detects network attacks using the latest machine learning algorithms and a convolutional neural network based on a dataset of the CSE-CIC-IDS2018. It is a recent dataset that contains a set of common and recent attacks. The detection rate is 99.7%, distinguishing between aggressive attacks and natural assertiveness.
Machine learning-based intrusion detection system for detecting web attacksIAESIJAI
The increasing use of smart devices results in a huge amount of data, which raises concerns about personal data, including health data and financial data. This data circulates on the network and can encounter network traffic at any time. This traffic can either be normal traffic or an intrusion created by hackers with the aim of injecting abnormal traffic into the network. Firewalls and traditional intrusion detection systems detect attacks based on signature patterns. However, this is not sufficient to detect advanced or unknown attacks. To detect different types of unknown attacks, the use of intelligent techniques is essential. In this paper, we analyse some machine learning techniques proposed in recent years. In this study, several classifications were made to detect anomalous behaviour in network traffic. The models were built and evaluated based on the Canadian Institute for Cybersecurity-intrusion detection systems dataset released in 2017 (CIC-IDS-2017), which includes both current and historical attacks. The experiments were conducted using decision tree, random forest, logistic regression, gaussian naïve bayes, adaptive boosting, and their ensemble approach. The models were evaluated using various evaluation metrics such as accuracy, precision, recall, F1-score, false positive rate, receiver operating characteristic curve, and calibration curve.
A new proactive feature selection model based on the enhanced optimization a...IJECEIAES
This document presents a new proactive feature selection (PFS) model to detect distributed reflection denial of service (DRDoS) attacks using an optimized feature selection approach. The PFS model uses swarm optimization and evolutionary algorithms like particle swarm optimization, bat algorithm, and differential evolution to select optimal features. It then evaluates selected features using machine learning classifiers like k-nearest neighbors, support vector machine, and random forest. The model was tested on the CICDDoS2019 dataset and achieved a high DRDoS detection accuracy of 89.59% while reducing the number of features. Evaluation metrics like accuracy, precision, recall and F1-score were also improved compared to previous models. The PFS model provides an effective approach
FLOODING ATTACKS DETECTION OF MOBILE AGENTS IN IP NETWORKScsandit
This document summarizes a research paper that proposes a new framework for detecting flooding attacks in mobile agent networks. The framework integrates divergence measures like Hellinger distance and Chi-square over a sketch data structure. The sketch data structure is used to derive probability distributions from traffic data in fixed memory. Divergence measures compare the current and prior probability distributions to detect deviations indicating attacks. The performance of detecting attacks while minimizing false alarms is evaluated using real network traces with injected flooding attacks. Experimental results show the proposed approach outperforms existing solutions.
System call frequency analysis-based generative adversarial network model for...IJECEIAES
In today's digital age, mobile applications have become essential in connecting people from diverse domains. They play a crucial role in enabling communication, facilitating business transactions, and providing access to a range of services. Mobile communication is widespread due to its portability and ease of use, with an increasing number of mobile devices projected to reach 18.22 billion by the end of 2025. However, this convenience comes at a cost, as cybercriminals are constantly looking for ways to exploit security vulnerabilities in mobile applications. Among the several varieties of malicious applications, zero-day malware is particularly dangerous since it cannot be removed by antivirus software. To detect zeroday Android malware, this paper introduces a novel approach based on generative adversarial networks (GANs), which generates new frequencies of feature vectors from system calls. In the proposed approach, the generator is fed with a mixture of real samples and noise, and then trained to create new samples, while the discriminator model aims to classify these samples as either real or fake. We assess the performance of our model through different measures, including loss functions, the Frechet Inception distance, and the inception score evaluation metrics.
The main goal of Intrusion Detection Systems (IDSs) is
to detect intrusions. This kind of detection system represents a
significant tool in traditional computer based systems for ensuring
cyber security. IDS model can be faster and reach more accurate
detection rates, by selecting the most related features from the
input dataset. Feature selection is an important stage of any IDs to
select the optimal subset of features that enhance the process of the
training model to become faster and reduce the complexity while
preserving or enhancing the performance of the system. In this
paper, we proposed a method that based on dividing the input
dataset into different subsets according to each attack. Then we
performed a feature selection technique using information gain
filter for each subset. Then the optimal features set is generated by
combining the list of features sets that obtained for each attack.
Experimental results that conducted on NSL-KDD dataset shows
that the proposed method for feature selection with fewer features,
make an improvement to the system accuracy while decreasing the
complexity. Moreover, a comparative study is performed to the
efficiency of technique for feature selection using different
classification methods. To enhance the overall performance,
another stage is conducted using Random Forest and PART on
voting learning algorithm. The results indicate that the best
accuracy is achieved when using the product probability rule.
NETWORK INTRUSION DETECTION AND COUNTERMEASURE SELECTION IN VIRTUAL NETWORK (...ijsptm
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to
rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus
or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection
System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data
created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for
anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack
signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with
the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System
called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in
detecting abnormal content in the traffic data during information passing from one node to another and
also detects known attack signature and unknown attack. This approach is tested by running the artificial
network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
Network Intrusion Detection And Countermeasure Selection In Virtual Network (...ClaraZara1
Intrusion in a network or a system is a problem today as the trend of successful network attacks continue to rise. Intruders can explore vulnerabilities of a network system to gain access in order to deploy some virus or malware such as Denial of Service (DOS) attack. In this work, a frequency-based Intrusion Detection System (IDS) is proposed to detect DOS attack. The frequency data is extracted from the time-series data created by the traffic flow using Discrete Fourier Transform (DFT). An algorithm is developed for anomaly-based intrusion detection with fewer false alarms which further detect known and unknown attack signature in a network. The frequency of the traffic data of the virus or malware would be inconsistent with the frequency of the legitimate traffic data. A Centralized Traffic Analyzer Intrusion Detection System called CTA-IDS is introduced to further detect inside attackers in a network. The strategy is effective in detecting abnormal content in the traffic data during information passing from one node to another and also detects known attack signature and unknown attack. This approach is tested by running the artificial network intrusion data in simulated networks using the Network Simulator2 (NS2) software.
This document provides an overview of a presentation titled "A Machine Learning Approach to Analyze Cloud Computing Attacks" given at the 5th International Conference on Contemporary Computing and Informatics. The presentation discusses introducing machine learning algorithms to detect various types of cloud computing attacks. It reviews previous work applying supervised, unsupervised, and reinforcement learning techniques for attack detection. The presentation concludes that machine learning provides an effective approach for cloud security but that more research is still needed, particularly for real-time attack detection and mitigation.
DDOS DETECTION IN SOFTWARE-DEFINED NETWORK (SDN) USING MACHINE LEARNINGIJCI JOURNAL
In recent years, the concept of cloud computing and the software-defined network (SDN) have spread
widely. The services provided by many sectors such as medicine, education, banking, and transportation
are being replaced gradually with cloud-based applications. Consequently, the availability of these
services is critical. However, the cloud infrastructure and services are vulnerable to attackers who aim to
breach its availability. One of the major threats to any system availability is a Denial-of-Service (DoS)
attack, which is intended to deny the legitimate user from accessing cloud resources. The Distributed
Denial-of-Service attack (DDoS) is a type of DoS attack which is considerably more effective and
dangerous. A lot of efforts have been made by the research community to detect DDoS attacks, however,
there is still a need for further efforts in this germane field. In this paper, machine learning techniques are
utilized to build a model that can detect DDoS attacks in Software-Defined Networks (SDN). The used ML
algorithms have shown high performance in the earliest studies; hence they have been used in this study
along with feature selection technique. Therefore, our model utilized these algorithms to detect DDoS
attacks in network traffic. The outcome of this experiment shows the impact of feature selection in
improving the model performance. Eventually, The Random Forest classifier has achieved the highest
accuracy of 0.99 in detecting DDoS attack.
USE OF MARKOV CHAIN FOR EARLY DETECTING DDOS ATTACKSIJNSA Journal
DDoS has a variety of types of mixed attacks. Botnet attackers can chain different types of DDoS attacks to confuse cybersecurity defenders. In this article, the attack type can be represented as the state of the model. Considering the attack type, we use this model to calculate the final attack probability. The final attack probability is then converted into one prediction vector, and the incoming attacks can be detected early before IDS issues an alert. The experiment results have shown that the prediction model that can make multi-vector DDoS detection and analysis easier.
Hybrid Technique for Detection of Denial of Service (DOS) Attack in Wireless ...Eswar Publications
Wireless Sensor Network (WSNs) are deployed at aggressive environments which are vulnerable to various security attacks such as Wormholes, Denial of Attacks and Sybil Attacks. There are various intrusion detection techniques that are used to identify attacks in a network with high accuracy level. This paper has focused on Denial of Service attack, since it is the most common attack that affects the environment severely. Therefore a new hybrid technique combining Hidden Markov Model with Ant Colony Optimization (HMM+ACO) has been
proposed that gives improved performance than the other techniques.
Visualize network anomaly detection by using k means clustering algorithmIJCNCJournal
With the ever increasing amount of new attacks in today’s world the amount of data will keep increasing,
and because of the base-rate fallacy the amount of false alarms will also increase. Another problem with
detection of attacks is that they usually isn’t detected until after the attack has taken place, this makes
defending against attacks hard and can easily lead to disclosure of sensitive information.
In this paper we choose K-means algorithm with the Kdd Cup 1999 network data set to evaluate the
performance of an unsupervised learning method for anomaly detection. The results of the evaluation
showed that a high detection rate can be achieve while maintaining a low false alarm rate .This paper
presents the result of using k-means clustering by applying Cluster 3.0 tool and visualized this result by
using TreeView visualization tool .
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
The document proposes a signature-based intrusion detection system using multithreading. It captures network packets and analyzes them for intrusions by comparing signatures to databases of known attacks. A multithreaded design is suggested to improve performance by processing packets in parallel threads. Agents would be deployed on the network with detection modules that use caching of frequent signatures to speed up analysis. An update module would transfer new frequent signatures to the caches.
Preemptive modelling towards classifying vulnerability of DDoS attack in SDN ...IJECEIAES
Software-Defined Networking (SDN) has become an essential networking concept towards escalating the networking capabilities that are highly demanded future internet system, which is immensely distributed in nature. Owing to the novel concept in the field of network, it is still shrouded with security problems. It is also found that the Distributed Denial-of-Service (DDoS) attack is one of the prominent problems in the SDN environment. After reviewing existing research solutions towards resisting DDoS attack in SDN, it is found that still there are many open-end issues. Therefore, these issues are identified and are addressed in this paper in the form of a preemptive model of security. Different from existing approaches, this model is capable of identifying any malicious activity that leads to a DDoS attack by performing a correct classification of attack strategy using a machine learning approach. The paper also discusses the applicability of best classifiers using machine learning that is effective against DDoS attack.
COPYRIGHTThis thesis is copyright materials protected under the .docxvoversbyobersby
COPYRIGHT
This thesis is copyright materials protected under the Berne Convection, the copyright Act 1999 and other international and national enactments in that behalf, on intellectual property. It may not be reproduced by any means in full or in part except for short extracts in fair dealing so for research or private study, critical scholarly review or discourse with acknowledgment, with written permission of the Dean School of Graduate Studies on behalf of both the author and XXX XXX University.ABSTRACT
With Fast growing internet world the risk of intrusion has also increased, as a result Intrusion Detection System (IDS) is the admired key research field. IDS are used to identify any suspicious activity or patterns in the network or machine, which endeavors the security features or compromise the machine. IDS majorly use all the features of the data. It is a keen observation that all the features are not of equal relevance for the detection of attacks. Moreover every feature does not contribute in enhancing the system performance significantly. The main aim of the work done is to develop an efficient denial of service network intrusion classification model. The specific objectives included: to analyse existing literature in intrusion detection systems; what are the techniques used to model IDS, types of network attacks, performance of various machine learning tools, how are network intrusion detection systems assessed; to find out top network traffic attributes that can be used to model denial of service intrusion detection; to develop a machine learning model for detection of denial of service network intrusion.Methods: The research design was experimental and data was collected by simulation using NSL-KDD dataset. By implementing Correlation Feature Selection (CFS) mechanism using three search algorithms, a smallest set of features is selected with all the features that are selected very frequently. Findings: The smallest subset of features chosen is the most nominal among all the feature subset found. Further, the performances using Artificial neural networks(ANN), decision trees, Support Vector Machines (SVM) and K-Nearest Neighbour (KNN) classifiers is compared for 7 subsets found by filter model and 41 attributes. Results: The outcome indicates a remarkable improvement in the performance metrics used for comparison of the two classifiers. The results show that using 17/18 selected features improves DOS types classification accuracies as compared to using the 41 features in the NSL-KDD dataset. It was further observed that using an ensemble of three classifiers with decision fusion performs better as compared to using a single classifier for DOS type’s classification. Among machine learning tools experimented, ANN achieved best classification accuracies followed by SVM and DT. KNN registered the lowest classification accuracies. Application: The proposed work with such an improved detection rate and lesser classification time and lar.
AN IMPLEMENTATION OF INTRUSION DETECTION SYSTEM USING GENETIC ALGORITHMIJNSA Journal
Nowadays it is very important to maintain a high level security to ensure safe and trusted communication of information between various organizations. But secured data communication over internet and any other network is always under threat of intrusions and misuses. So Intrusion Detection Systems have
become a needful component in terms of computer and network security. There are various approaches being utilized in intrusion detections, but unfortunately any of the systems so far is not completely flawless. So, the quest of betterment continues. In this progression, here we present an Intrusion
Detection System (IDS), by applying genetic algorithm (GA) to efficiently detect various types of network intrusions. Parameters and evolution processes for GA are discussed in details and implemented. This approach uses evolution theory to information evolution in order to filter the traffic data and thus reduce the complexity. To implement and measure the performance of our system we used the KDD99
benchmark dataset and obtained reasonable detection rate.
NTRUSION D ETECTION S YSTEMS IN M OBILE A D H OC N ETWORKS : S TATE OF ...ijcsa
Mobile Ad Hoc Networks (MANETs) are more vulnerable
to different attacks. Prevention methods as
cryptographic techniques alone are not sufficient t
o make them secure; therefore, efficient intrusion
detection must be deployed and elaborated to facili
tate the identification of attacks. An Intrusion De
tection
System (IDS) aims to detect malicious and selfish n
odes in a network. The intrusion detection methods
used
normally for wired networks can no longer adequate
when adapted directly to a wireless ad-hoc network,
so existing techniques of intrusion detection have
to be changed and new techniques have to be determi
ned
to work efficiency and effectively in this new netw
ork architecture of MANETs. In this paper we give a
survey of different architectures and methods of in
trusion detection systems (IDSs) for MANETs
accordingly to the recent literature.
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P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service Attack Detection for Vehicular Communication Network Resource Management
1. International Journal of Computing and Digital Systems
ISSN (2210-142X)
Int. J. Com. Dig. Sys. 12, No.1 (Nov-2022)
https://dx.doi.org/10.12785/ijcds/1201105
P2PCPM: Point to Point Critical Path Monitoring Based
Denial of Service Attack Detection for Vehicular
Communication Network Resource Management
Vartika Agarwal1
and Sachin Sharma1
1
Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun,India
Received 31 Dec. 2021, Revised 7 Sep. 2022, Accepted 31 Oct. 2022, Published 30 Nov. 2022
Abstract: Various types of security attacks are normal in vehicular communication networks. The current study uses a support vector
machine to implement a Point to Point Critical Path Monitoring (P2PCPM) based Denial of Service (DOS) Attack detection technique
for Vehicular Communication Network (VCN) resource management. Greatest quality of P2PCPM is that it eliminates attacked nodes
from the network for the smooth process of vehicular communication. This scheme works well in terms of accuracy as well as attack
detection rate. The whole simulation is made and tried by utilizing MATLAB Software. Simulation result shows 99% accuracy in case
of security attack detection as well as reduced training and testing error upto 2%. Experimental results indicate that this scheme has a
great efficiency and works well up to 1000 nodes, which is the limitation of current implementation. In future, simulation test may be
done for unlimited nodes using similar or other techniques of attack detection.
Keywords: Denial of Service (DOS), Distributed Denial of Service (DDOS), Point to point Critical Path Monitoring (P2PCPM), Support
Vector Machine (SVM),Vehicular Communication Network (VCN)
1. Introduction
VCN is a network which establish communication be-
tween different vehicles. Sensors, transmitters and other re-
sources act as medium of communication between vehicles.
Such resources have power to share message about traffic
and any other emergency. Security attacks such as Spyware,
Worms and DOS disrupt this network [1]. Identification of
such types of attack is required for smooth working of ve-
hicular communication. Sometimes message or information
may be delay because of such attacks. This delay will result
in an accident, and a significant amount of time will be
wasted in traffic. We use DOS attack detection technique
for vehicular communication network which is based on
P2PCPM. DOS is an unauthorised attempt to crash the
vehicular communication network and making it difficult
to reach to its actual users [2]. In this research, a vehicle
is represented by sensor node. Radio transmissions can be
used to communicate between sensor nodes. We create a
training model to train the Support Vector Machine (SVM)
learning model, and then we test it on real-world data to
find the attacked nodes and calculate both the training and
testing errors. DOS attacks are generated by any person,
organisation for crash the network. Such unauthorised users
send same information again and again by using the network
resources. Due to this unauthorised activity, actual users
unable to access the network. Such attacks can be mostly
found in network and transport layer. There are two kinds of
DOS attack – Jamming as well as Tampering. In Jamming,
legitimate user (attacker) tries to break the network. . In
Tampering, attackers target the sensor nodes. E-Commerce
websites, VCN, or any online service provider are the
main target of attackers. For preventing DOS attacks we
generate a strong vehicular communication network and
detect DOS attacked node and remove it from the network.
The importance of DOS detection are as follows [18]:
DOS attack is responsible for slow down the communication
between vehicles. So for the faster response, it is needed to
recognize vehicles which are responsible for DOS attack.
DOS damage network resources and generate a lot of fake
signals. The result of the fake signal is authorized vehi-
cles are unable to communicate. DOS hangs the complete
system and stops the whole communication process. DOS
attacks are launched by an attacker. They generate malicious
packets and increase the network load.
Main contribution in this paper are as follows:
Generate a P2PCPM based vehicular communication net-
work. Detecting attacked nodes from the network Elimi-
nate such kind of nodes and repeat the process until all
attacked nodes are not identified. Ensure smooth work of
the network vehicular communication process. The research
organization is done as follows: Section II depicts the
related work in this research. Section III present method
and implementation of proposed system. Section IV discuss
about performance and result of this simulation. Section V
E-mail address: vartikaagarwal2015@gmail.com, sachin.cse@geu.ac.in, http://journals.uob.edu.bh
2. 1306 Vartika Agarwal, et al.: P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service..
conclude the work with summary and highlight the future
scope of proposed system.
2. Literature Review
Several experts have worked for the security attacks in
last two decades(Table I). In 2017, Myeongsu Kim propose
mechanism of security attack detection in a software de-
fined network. Experimental result validate that this scheme
works well for identifying types of cyber attack [1]. In
2017, Narmeen Zakaria Bawany, propose novel structure
for security attack identification in SDN. This framework
is beneficial for smart cities where there a huge chances of
such types of attacks [2] . In 2017, Tasnuva reviewed about
techniques of security attack detection and prevention. They
highlight the impact of techniques, challenges as well as ex-
perimental model used by different authors for such research
[3]. In 2017, Mohamed Idhammad design deep learning
based method of DOS identification. This technique has a
great accuracy and it outperform other detection techniques
in terms of security [4]. In 2017, Chuan long propose
intrusion detection system by using deep learning. It has
a great accuracy rate and better result in comparison of
other security techniques [5]. In 2018, Lei SU introduced a
supervisory model for detecting the attacker behavior. They
analyze the performance of this technique by using attack
success rate and packet reception rate [6]. In 2018, Shailen-
dra Rathore use supervised machine learning algorithm for
identifying attacked nodes as well as normal nodes. It has
86% accuracy and achieve better performances then the
other security attack detection framework [7].In 2018, Julio
Navarro explain mechanism about how attackers attack on
the network and break the security features. They discuss
about how to recognize such types of cyber attack [8].In
2018, Yunsheng Fu propose attack detection model based
on LSTM and RNN .They use Bayesian theorem for train
the neural network . Experimental result validate that it has
80% accuracy and less network drop rate [9].In 2018, Gao
Liu review about various security attacks , previous work
on security measures and point out future research direction
[10].In 2019, Francisco present DOS attack detection model
It has a great accuracy, high precision as well as low false
alarm rate [11]. In 2019, Gradient descent algorithm are
proposed by Gayathri for identification of security attacks.
This algorithm has achieved 97% accuracy which is far
better than other intrusion detection system [12]. In 2020,
Bombang present security attack detection model which is
based on machine learning. It has a great accuracy, better
throughput as well as faster response time. [13]. In 2020,
deep neural network was presented by Sumitha for security
attack detection. It has less network drop rate as well as
great efficiency [14]. In 2020, novel tensor based structure
are proposed by Joao Palo for security attack identification
using machine learning concept.This framework provides
better throughput and achieves 95% accurate results [15]. In
2020, Swathi use CICIDS 2017 dataset for identification of
attacked nodes. This scheme has achieved 73.79% accuracy
but failed to reduce training and testing error [16]. In
2021, Arnold Adimabua Ojugo use deep neural network
for attacked nodes identification. It works well in case
of accuracy as well as elapsed time [17].In 2020, Bavani
K use mathematical model for distributed DOS detection.
This model has the capability to work well upto 500 data
packets.Its accuracy rate is 97% [18]. In 2021, Sungwoong
present LSTM based attack detection model. It has a 92%
attack detection rate and 20% false positive rate [19]. In
2021, Deepak Kshirsagar introduced weight based reduction
method for security attacks identification and prevention. It
has a great accuracy which is upto 90% [20]. In 2020, Ade-
mola P. Abidoye develop lightweight model for detection
of DOS attack in wireless sensor network. Experimental
result verify the accuracy and effectiveness of this system
. They use network simulator NS3 for simulation of this
scheme. It takes lot of time to train and test the model [21].
In 2021, Yasser Alharbi propose KNN algorithm for DOS
attack detection. This algorithm improve attack detection
performance of IPv6 network. This algorithm calculate
distance between two sample points and finally get the
attacked nodes. It has some deficiencies such as inaccuracy,
false positive rate and it takes more time to identify attacked
nodes [22]. In 2021, Dan Zhang present survey on attack
detection and review about Deception attack and ICMP
attacks. It includes advantage, disadvantage, conclusion and
various methodologies [23]. In 2021, Amiya Kumar Sahu
propose deep learning based mechanism to detect security
attack in IOT devices. Its accuracy rate is 96% [24]. In
2021, Jun Zhang propose deep learning solution for cyber
attack detection. They use several high quality dataset for
simulation of such problem. They discuss challenges, short-
comings and future scope of such research [25]. In 2021,
Bilal Alhayani investigate about different kinds of cyber
security attacks like denial of service, phishing attacks etc.
They plan strategies and apply in the network for security
of data from different kinds of malware [26]. In 2022,
Christopher Regan propose federated based approach to
detect botnet attacks. Simulation result shows 98% accuracy
rate and has a great performance over traditional methods
[27]. In 2022, Sandeep Kautish propose DDOS strategy for
cloud computing environment when we compare it with
existing methods, it’s accuracy is 96% [28]. In 2022, Qiuhua
Wang focus deep learning based approach for cyber attack
detection. Experimental result validate that it has 85% accu-
racy rate and are able to detect malicious modes within 200
seconds [29]. In 2022, Kim-Hung-Le present an intrusion
detection software to protect vehicular communication from
different kind of cyber attack such as wormhole, Backdoor
etc [30].In 2022,vartika agarwal investigate about deep
learning technique to improve RRM in VCN.They highlight
various algorithm for resource allocation [31]. In 2022,
Vartika Agarwal highlight multitype vehicle identification
scheme from real time traffic database and offer subscription
plan for its user[32].
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3. Int. J. Com. Dig. Sys. 12, No.1, 1305-1314 (Nov-2022) 1307
TABLE I. Comparative Study
Author Proposed Scheme Advantage Limitation or Future Scope
[1] Security attack detection in SDN 70% accuracy, High precision High packet drop rate as well as
training and testing error
[2] Survey of DDOS attack detection
technique
Elaborate the Different kind of cy-
ber security attacks
Results under this survey are less
accurate.
[3] Review on Cyber attacks Details about effect of cyber at-
tacks on a network
Future work on Cyber attacks on
cloud , homes as well as IOT based
systems.
[4] Artificial Neural Network (ANN)
for detecting cyber attacks
Offer satisfactory result in case of
accuracy as well as detection time
Upgrade ANN for better accuracy.
[5] Intrusion detection scheme with re-
current neural network
80% accuracy as well as less net-
work drop rate.
Training time and testing time
should be reduced.
[6] Supervisory strategy for detecting
attacker behaviour.
Packet reception rate is high. Works only for limited no of nodes.
[7] Supervised Machine learning algo-
rithm for identifying normal nodes
as well as an attacked nodes.
86% accuracy and achieve better
performance.
Works only for NSL-KDD
datasets.
[8] Reviewed about different kind of
cyber-attacks.
Covering 80 methods for analysing
attacks.
In future, detection and prevention
mechanism should be explored.
[9] Attack detection through LSTM
and RNN.
80% accuracy and less false posi-
tive rate.
Modify the proposed system for
wormhole attack detection.
[10] Attack detection in mobile adhoc
network .
Different attack detection tech-
niques are elaborated.
In future, explore some recent chal-
lenges in MANET.
[11] Smart DOS attack detection system Accuracy, high precision and low
false alarm rate.
Modify this approach for better de-
tection rate.
[12] Gradient descent algorithm for cy-
ber security attack.
Reduce training as well as testing
error.
Use deep learning approach for
more accurate result.
[13] Machine learning algorithm for se-
curity attack detection.
Great result in term of accuracy as
well as faster response time.
Combine several algorithm for bet-
ter result.
[14] Deep neural network for DOS at-
tack detection
Less packet loss, less overhead as
well as better throughput
Modify this approach for better re-
sult and implementing in real time
environment.
[15] Tensor based model for DDOS de-
tection.
throughput rate is good as well as
accurate result
Packet drop rate is too high.
[16] DDOS attack detection on CICIDS
2017 datasets.
73% accuracy as well as less
packet loss.
Reduce training and testing error.
[17] Deep neural network for prevention
of security attack.
70% accuracy and better through-
put.
Use RNN or LSTM for better re-
sult.
[18] DDOS detection in SDN Highly accurate and work for 500
data packets.
Increase no of packets for better
research.
[19] LSTM based security attack detec-
tion.
92% attack detection rate and 20%
false positive rate.
Reduce packet drop rate and in-
crease the accuracy.
[20] Weight based reduction method for
security attack detection.
90% accuracy rate as well as less
packet loss.
Apply this technique for different
dataset.
Proposed Scheme. 99% attack detection accuracy as
well as 2% reduction in train-
ing/testing error.
We can use this scheme to de-
tect further attacks such as node
replication, wormhole etc.
http://journals.uob.edu.bh
4. 1308 Vartika Agarwal, et al.: P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service..
Figure 1. SVM for DOS detection
3. Methodology and Implementation
Our research focuses on DOS detection in a vehicular
communication network using a support vector machine
(SVM).The main objective of this research is to eliminate
those nodes which disrupt the vehicular communication
network.
i. Vehicular Communication Network Resource
Management: It contains thousands of sensors of nodes.
The node is equipped with various sensing devices and
have a limited processing speed and storage capacity. In
this simulation, we have to use sensor data that are taken
from vehicles. Message Size which was transmitted by the
attackers.
ii. Support Vector Machine: It is an algorithm that learns
by example to assign tag to nodes. In proposed research
SVM identify DOS attack by sensor reading and message
size. After taking data, it recognizes those nodes which
is responsible for communication interruption. It remove
those nodes and offer safer communication.
From Figure 1, we can see that SVM recognize no of
nodes (Vehicles) in VCN then SVM calculate distance
from one nodes to another nodes. After it SVM takes data
from sensor reading and message size and generate training
set. After generating training set. SVM check nodes and
classify it into two categories. If they are attacked nodes
mark it with red, remove it from VCN . If there are no
attacked nodes, vehicular communication process works
smoothly. This process continue until all attacked nodes
are not detected.
iii. System Configuration: Intel i5 processor with 8
Figure 2. Flow chart of P2PCPM based DOS attack detection for
vehicular communication network resource management
GB RAM support this experiment. MATLAB 2020 have
best features which are used for simulation for this research.
The steps for research design are shown in Figure 2.
In this figure, we can see the whole process from vehicular
communication network generation to attack detection. In
every round, we can see that attacked nodes are detected and
speed up the communication process between automobiles.
A. Network Specification: It includes all the inputs and
output generated by user or system. User have to enter
length and width of an area, reading of sensor, size of
message etc. After entering input , system provide no of
attacked nodes, no of rounds etc. (Table II) (Figure3)
TABLE II. SIMULATION PARAMETERS CONSIDERED IN IM-
PLEMENTATION
Parameters for Simulation Value
Number of Nodes 100
Area Length 500
Area Width 500
Sensor Data 25
Message Size 500 bytes
Dimensions 1000*1000
Percentage of Attacked Nodes up to 2%
Maximum no of rounds 50
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5. Int. J. Com. Dig. Sys. 12, No.1, 1305-1314 (Nov-2022) 1309
Figure 3. Simulation and Set up of Vehicular communication network resource management
B. Network Nodes Specification: After network specification, nodes according to the specification is generated. After the
generation of nodes, the path of VCN is generated. (Table III) (Figure 4).
After generating critical path of vehicular communication network. We can get specification Evaluation matrix of
point to point critical path network (Table IV).
TABLE III. POINT TO POINT CRITICAL PATH OF NETWORK NODES SPECIFICATION
S.No Starting Point mid point End Point Radius
Longitude Latitude Longitude Latitude
X0 29.9457o
N 78.1462o
E 30.1311 30.3165o
N 78.0322o
E 0.1854
X1 29.9458o
N 78.1462o
E 30.1311 30.3165o
N 78.0322o
E 0.1853
X2 29.9459o
N 78.1462o
E 30.1312 30.3165o
N 78.0322o
E 0.1853
X3 29.9460o
N 78.1462o
E 30.1312 30.3165o
N 78.0322o
E 0.1852
X4 29.9461o
N 78.1462o
E 30.1312 30.3165o
N 78.0322o
E 0.1852
X5 29.9462o
N 78.1462o
E 30.1313 30.3165o
N 78.0322o
E 0.1851
X6 29.9461o
N 78.1462o
E 30.1314 30.3165o
N 78.0322o
E 0.1851
X7 29.9464o
N 78.1462o
E 30.1314 30.3165o
N 78.0322o
E 0.1877
X8 29.9465o
N 78.1462o
E 30.1315 30.3165o
N 78.0322o
E 0.185
X9 29.9466o
N 78.1462o
E 30.1315 30.3165o
N 78.0322o
E 0.184
C. Generate Training Set: After VCN generation, training set will be created and this model is verified on data very
similar to real sensing data to check the power of this model to eliminate attacked nodes and calculate training as well as
testing error.
http://journals.uob.edu.bh
6. 1310 Vartika Agarwal, et al.: P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service..
Figure 4. Vehicular communication network generation
Figure 5. DOS attack detection in vehicular communication network
resource management
TABLE IV. Specification evaluation matrix of P2PCPM (N=10)
Statistics Result
Squared Deviation 8.9402
Variance 9.8
Covariance 33.479
Standard Deviation 500
Coefficient of correlation 10.69
Figure 6. Normal nodes vs attacked nodes in vehicular
communication network resource management
Figure 7. Proposed Methodology (P2PCPM)
D. DOS Detection: After Generating Datasets, we
identified those vehicles which suffer from DOS attack.
Red nodes will be marked as attacked nodes (Figure 5).
From Table V, we can see that there are 6 bad nodes
whose sensor data is above 25 and message size is 500.
This process continues until bad nodes are detected
After the elimination of attacked nodes, speed of communi-
cation between vehicles will be increasing. From Figure 6,
we see that dotted line represent attacked nodes and after
identification of attacked nodes communication between ve-
hicles increasing continuously.Here two different scenarios
are the normal node and attacked node. In a Normal node,
communication between nodes will continue otherwise in
DOS attacked node, the message passing through that nodes
automatically stops during the simulation of n no of nodes.
http://journals.uob.edu.bh
8. 1312 Vartika Agarwal, et al.: P2PCPM: Point to Point Critical Path Monitoring Based Denial of Service..
4. Performance Evaluation and Discussion
For performance evaluation, we use following parameters
accuracy, throughput and elapsed time. These parameters
are basically used for validating the performance of the
system.
Accuracy = (Attacked Nodes Detected*100)/(Attacked
Nodes Present) 99% (Figure 7)
False Positive Rate (FPR) - It is the ratio of DOS
Attacked nodes and those nodes which are classified as
normal by mistake but belong to DOS attack.
FPR = (Number of misclassified DOS attacked
nodes*100)/(Actually Attacked Nodes) = 1%
Elapsed Time - It means the time taken by the software to
detect attacked nodes = 128 Seconds.
From Table VI, [12] author used gradient descent algorithm
for identifying DOS attack. It has limitation that it works
only for 41 nodes. Its takes 332 sec to train and 328 sec
to test the model. [16] author used CICIDS 2017 dataset
for DDOS detection but has less accuracy and takes lot of
training as well as testing time. [17] author classify data
packets into malicious and non malicious data packets. Its
accuracy rate is 70% which is very less. [18] author detect
DDoS attack in software defined network. It has 97%
accuracy but it has limitation that it can identify malicious
node upto 500 data packets. [19] author detect DOS attack
using LSTM technique. It has 92% accuracy and 20%
false positive rate. It takes 160 sec to train the model and
158 sec to test the model which is more in comparison of
proposed scheme. [20] author propose intrusion detection
system and reduce DDOS attack with 90% accuracy.
limitation of this scheme is that it works only for CICIDS
2017 datasets.From above references, we can validate
that proposed scheme works for more than 1000 nodes
and provide 99% accurate result. Its false positive rate
is 16% and reduces training as well as testing error upto 2%.
5. Conclusion and Future Scope
Major research gap we found that any scheme would
not work for more than 500 nodes and there is a lack of
accuracy. In the proposed approach, we can check DOS
attack up to 1000 vehicle nodes. In this research, we are
checking DOS attack upto 100 nodes out of which 6 nodes
are attacked nodes. we can check attacked nodes again
and again after changing network specification. Success
rate is 99% in detecting DOS attacks. It takes 132 seconds
for model training and 128 seconds for model testing.
After detecting attacked nodes, communication process
work smoothly(Figure.7). This scheme works well in
comparison of other security detection models (Table VI).
Experimental results demonstrate that this methodology
offer more accurate outcomes. In future, we can use this
scheme to detect further attacks such as wormhole,Node
replication attack etc.
TABLE VI. Performance comparison of P2PCPM Based DOS
detection with existing methodologies
Nodes
Datasets
Accuracy Training
time
Testing
Time
Reference
41 97.7% 332 Sec 328 Sec [12]
CICIDS
2017
70% 288 Sec 285 Sec [16]
CICIDS
2017
70% 175 Sec 173 Sec [17]
500
Data
Packets
97% 150 Sec 147 Sec [18]
2018
Korea
92% 160 Sec 158 Sec [19]
CICIDS
2017
90% 170 Sec 168 Sec [20]
1000 99% 132 Sec 128 Sec Proposed
Scheme
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“Vartika Agarwal received degree of Bach-
elor of Computer Application from Surajmal
Agarwal Pvt Kanya Mahavidyalaya, Kichha,
and Uttarakhand, India. She obtained master
of computer application degree from Shri
Ram Murti Smarak College of Engineer-
ing and Technology (SRMS), Bareilly, Ut-
tar Pradesh India. She is currently pursuing
Ph.D. in Computer Science and Engineering
Department from Graphic Era Deemed to be University, Dehradun,
Uttarakhand, India. Her research interests include Internet of
Things, Image Processing, Computer Vision etc.”
“Sachin Sharma, Associate Professor , De-
partment of CSE at Graphic Era (Deemed to
be) University, Dehradun, Uttrakhand, India.
He is Co-founder of IntelliNexus LLC. He
has completed his Ph.D. from University of
Arkansas at little rock in the subject of en-
gineering science and system specialization.
He has a great knowledge about wireless
communication networks, IOT, Vehicular ad-
hoc networking and network security”
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