ID:21512
Type of Publication: Conference Proceedings
Authors: Asaf Shabtai,
Title: Applying machine learning techniques for detection of malicious code in network traffic
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Year: 2007
Volume: 4667 NAI
Pages: 44 - 50
Publisher: Springer
Abstract: 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. © Springer-Verlag Berlin Heidelberg 2007.
Keywords: Algorithms;Bayesian networks;Feature extraction;Neural networks; ,
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