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1. CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249)
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BIG DATA ANALYTICS FOR USER-ACTIVITY ANALYSIS AND USER-ANOMALY
DETECTION IN MOBILE WIRELESS NETWORK
ABSTRACT
The next generation wireless networks are expected to operate in fully automated fashion to meet
the burgeoning capacity demand and to serve users with superior quality of experience. Mobile
wireless networks can leverage spatiotemporal information about user and network condition to
embed the system with end-to-end visibility and intelligence. Big data analytics has emerged as a
promising approach to unearth meaningful insights and to build artificially intelligent models
with assistance of machine learning tools. Utilizing aforementioned tools and techniques, this
paper contributes in two ways. First, we utilize mobile network data (big data) – call detail
record (CDR) – to analyze anomalous behavior of mobile wireless network. For anomaly
detection purposes, we use unsupervised clustering techniques namely k-means clustering and
hierarchical clustering. We compare the detected anomalies with ground truth information to
verify their correctness. From the comparative analysis, we observe that when the network
experiences abruptly high (unusual) traffic demand at any location and time, it identifies that as
anomaly. This helps in identifying regions of interest (RoI) in the network for special action such
as resource allocation, fault avoidance solution etc. Second, we train a neural-network based
prediction model with anomalous and anomaly-free data to highlight the effect of anomalies in
data while training/building intelligent models. In this phase, we transform our anomalous data
to anomaly-free and we observe that the error in prediction while training the model with
anomaly-free data has largely decreased as compared to the case when the model was trained
with anomalous data.
EXISTING SYSTEM:

2. CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249)
MAIL ID: , praveen@nexgenproject.com
Web: www.nexgenproject.com,
In the literature, anomaly detection has been performed through various supervised, semi-
supervised and unsupervised learning methods. Authors proposed a framework that categorizes
large size CDR into different call profile and accordingly classify network usages. As a by-
product, the proposed framework identifies unexpected traffic that is anomalous as compared to
the normal. They consider large-scale dataset of voice calls, however, our dataset is comprised
of voice calls as well as text messages. Article categorized anomaly detection techniques for
Big Data based on nearest neighbors, clustering and statistical approaches. Their finding suggests
that for real datasets, clustering based anomaly detection technique performs the best and second
best performance was attained by the nearest neighbor based technique k-NN -, k-means
clustering approach has been performed on CDR for different purposes such as identifying
Industrial Parks & Office Areas, Commercial Areas, Nightlife areas, Leisure areas and
Residential areas. It was validated that there was a strong correspondence between land uses and
the infrastructures included in the geographical representation of each cluster. Using the k-
means clustering, the authors were able to detect volume anomalies in real network traffic,
achieving satisfactory results. K-means clustering has been applied for anomaly detection in
traffic data. The data contains unlabeled flow records, which k-means algorithm separates into
clusters of normal and anomalous traffic. The authors utilize agglomerative hierarchical
clustering to perform segmentation and anomaly/outlier detection from a single dendrogram,
utilizing Pearson correlation as distance function. Rule based approach has been utilized have
been for detecting anomaly CDR information generated from wireless network. However, study
overlooks the effect of non-travelling users in the analysis, which is a common case in business
areas. On the contrary in this paper, we first categorize the call activity information into different
clusters. This in turn helps to identify the un-natural behavior in the activity on a particular date
and time, which further can be traced back for root cause analysis. Few similar works along with
security concern are also presented PROPOSED SYSTEM:
In this paper, we exploit call detailed record (CDR) information collected from core network
(CN) of a real mobile cellular network. The patio-temporal information contained within the

3. CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249)
MAIL ID: , praveen@nexgenproject.com
Web: www.nexgenproject.com,
CDR helps us analyze user specific activity in a certain region at a particular time and date. By
anomaly in this paper, we mean abnormal or unusual behavior or activity pattern of user and thus
an accordingly effect on the network. Anomaly in network performance can be noticed due to
multiple reasons such as sleeping cell, hardware failures, surge in traffic etc. Successful anomaly
detection and its proper treatment results in multiple benefits, e.g., at a place like stadium, when
the provided bandwidth resource does not suffice the highly-dense user demands then it will
cause choke in bandwidth, and it will appear as anomaly in the network. On one hand, such
behaviors are considered normal in telecom and are not usually counted amongst anomalies. On
the other hand, categorizing them into anomalies would help the network operators not only in
detecting them but also in determining the region of interests (ROI), for which proper network
resources can be proactively allocated for enhanced quality of experience. Our contributions
towards anomaly detection in this paper are as follows. 1. We utilize machine-learning tools
namely K-means clustering and Hierarchical clustering to detect anomalies. 2. We verify and
compare the anomalies with those found through ground truth observation to determine their
accuracy. 3. After anomaly detection and verification, the performance of anomaly-free data is
evaluated by passing it through a neural network based prediction model that forecasts future
traffic pattern. We observe that the mean square error between the training, validation and test
data is largely reduced as compared to that in the case when the model was trained with
anomalous data.
CONCLUSION AND FUTURE WORK
In this paper we presented anomaly detection in mobile network big data (Call Detail Record:
CDR) using machine learning (clustering) technique. We analyzed user activities at different
time and location from the patio-temporal information contained within CDR. Using k-means
and hierarchical clustering techniques. The user activities that were unusually high caused
unusual traffic demand and thus were categorized into anomalies. Further, after verifying these
anomalies with ground truth information, we found that the location where there was unusual

4. CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249)
MAIL ID: , praveen@nexgenproject.com
Web: www.nexgenproject.com,
high traffic was a stadium and at the time when the network experienced high demand, there was
a soccer match going on. And thus the traffic experienced was higher than on usual days. Thus
with the help of anomaly detection, region of interests (Roil) can be identified, for which proper
action (e.g. proper resource allocation) can be taken in advance to meet the requirements. We
also discussed the effect of anomalous and anomaly-free data by experimenting on a prediction
model. We found that training the model with anomaly-free data resulted in less mean square
error than with anomalous data. This work can be extended to understand the dynamics of user
in envisioned smart cities. Big data analytics approach can be utilized to understand users’
contextual information such as mobility pattern, traffic pattern, their choice of contents, their
social network and ties etc. By extracting these insightful information, smart and intelligent
resource allocation algorithms can be developed for efficient resource utilization. Additionally,
data analytics can also be performed on data collected from energy meters in every home to
understand and appropriately determine energy utilization pattern in smart cities.
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5. CONTACT: PRAVEEN KUMAR. L (,+91 – 9791938249)
MAIL ID: , praveen@nexgenproject.com
Web: www.nexgenproject.com,
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