As the technical skills and costs associated with the deployment of phishing attacks decrease, we are witnessing an unprecedented level of scams that push the need for better methods to proactively detect phishing threats. In this work, we explored the use of URLs as input for machine learning models applied for phishing site prediction. In this way, we compared a feature-engineering approach followed by a random forest classifier against a novel method based on recurrent neural networks. We determined that the recurrent neural network approach provides an accuracy rate of 98.7% even without the need of manual feature creation, beating by 5% the random forest method. This means it is a scalable and fast-acting proactive detection system that does not require full content analysis.
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Classifying Phishing URLs Using Recurrent Neural Networks
1. Classifying Phishing URLs Using
Recurrent Neural Networks
Sergio Villegas
Javier Vargas
*Alejandro Correa Bahnsen
Easy Solutions Research
Eduardo Contreras Bohorquez
Fabio A. Gonzalez
MindLab Research Group,
Universidad Nacional de
Colombia
2. Industry recognition
A leading global provider of electronic
fraud prevention for financial institutions
and enterprise customers
385 customers
In 30 countries
100 million
Users protected
27+ billion
Online connections monitored
About Easy Solutions®
Easy Solutions to be Acquired by New Joint Venture Creating Global, Secure Infrastructure Company
3. Phishing
3
Phishing is the act of defrauding an online
user in order to obtain personal information
by posing as a trustworthy institution or
entity.
8. Ideal Phishing Detection System - Issues
8
Issues with full content
analysis:
• Time consuming
• Impractical to process
millions of websites per day
• Hard to implement for
small devices
20. The Problem of Long-Term Dependencies
20
Short term dependencies are easy
long term …
21. Long-Short Term Memory Networks LSTM
21
RNN contains
a single layer
LSTM contains
four interacting
layers
Source: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
31. What we learned
• Discerning URLs by their patterns is a good predictor of
phishing websites
• LSTM model shows an overall higher prediction
performance without the need of expert knowledge to
create the features
31