Polymorphic Worms Detection Using A Supervised Machine Learning Technique
نویسندگان
چکیده
Polymorphic worms are considered as the most dangerous threats to the Internet security, and the danger lies in changing their payloads in every infection attempt to avoid the security systems. We have designed a novel doublehoneynet system, which is able to detect new worms that have not been seen before. To generate signatures for polymorphic worms we have two steps. The first step is the polymorphic worms sample collection which is done by a Double-honeynet system. The second step is the signature generation for the collected samples which is done by using a Support Vector Machines (SVMs) technique. The system is able to generate accurate signatures for single and multiple worms. Keywords-honeynet; worms; machine learning algorithm.
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