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In this research, we consider the related problem of malware classification based on HMMs. We train HMMs for a variety of malware generators and a variety of compilers. The results of HMM are further classified using k means algorithm but k means algorithm has drawback of stuck into local minima so we optimized the k means with genetic algorithm (GA). Genetic algorithm (GA) tuned k means clustering...
Metamorphic malware are able to change their appearance to evade detection by traditional anti-malware software. One of the ways to help mitigate the threat of new metamorphic malware is to determine their origins, i.e., the families to which they belong. This type of metamorphic malware analysis is not typically handled by commercial software. Moreover, existing works rely on analyzing the op-code...
In just a few short years, the number of polymorphic and metamorphic malware samples seen in the wild has grown exponentially, and the automated malware detection apparatus which is largely signature-based finds itself virtually and practically useless for these new types of attacks. New detection methods are needed in order to better defend networks, protect data and preserve overall internet operations...
Deciding if a given program is malicious or not is a recurring problem in anti-malware research, giving the fact that it is generally undecidable. Although field experts are able to perform correct classifications, the amount of both clean and malicious samples that appear every day is too high for relying only on manual analysis. In practice, the files collections are clustered and intensive analysis...
In recent years, attackers have started to use web pages to deliver their malicious code to users. Web-based malware overcomes signature-based detection by modification of the code or using zero-day exploits. We propose a malicious activity detection method using Hidden Markov Models (HMM) alongside a client honeypot system. Our algorithm is able to detect the potential malicious behaviour of a web...
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