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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...
Previous research has shown that hidden Markov model (HMM) is a compelling option for malware identification. However, some advanced metamorphic malware have proven to be more challenging to detect with these techniques. In this paper, we separated the importance of the some part of the malware files to train the HMMs aiming at extracting the significant sequences of malware opcodes. These parts have...
Computer virus is a rapidly evolving threat to the computing community. These viruses fall into different categories and it is generally believed that metamorphic viruses are extremely difficult to detect. The first step to effectively combat a virus is to successfully classify it's family so that past experience can be readily applied to understand it's functionality and apply the right strategy...
Attacks such as XSS and SQL injections are still common in browser extensions due to the presence of potential vulnerabilities in extensions and some extensions are also malicious by design. As a consequence, much effort in the past has been spent on detecting vulnerable and malicious browser extensions. These techniques are limited to only detect either new forms of vulnerable or malicious extensions...
Recent work has presented hidden Markov models (HMMs) as a compelling option for virus identification. However, to date little research has been done to identify the meaning of these hidden states. In this paper, we examine HMMs for four different compilers, hand-written assembly code, three virus construction kits, and a metamorphic virus in order to note similarities and differences in the hidden...
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