Yuval Elovici

Senior Academic

Applying machine learning techniques for detection of malicious code in network traffic

The Early Detection, Alert and Response (eDare) system is aimed at purifying Web traffic propagating via the premises of Network Service Providers (NSP) from malicious code. To achieve this goal, the system employs powerful network traffic scanners capable of cleaning traffic from known malicious code. The remaining traffic is monitored and Machine Learning (ML) algorithms are invoked in an attempt to pinpoint unknown malicious code exhibiting suspicious morphological patterns. Decision trees, Neural Networks and Bayesian Networks are used for static code analysis in order to determine whether a suspicious executable file actually inhabits malicious code. These algorithms are being evaluated and preliminary results are encouraging.

Publication language English
Pages 44-50
Publication status Published - 01.01.2007

Keywords

Feature selection
Machine learning
Malicious code
Network Service Provider (NSP)

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science
Other files and links
Link to publication in Scopus