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In this paper, we analyze several metamor-phic virus generators. We define a similarity index and use it to precisely quantify the degree of metamorphism that each generator produces. Then we present a detector based on hidden Markov models and we consider a simpler detection method based on our similarity index. Both of these techniques detect all of the(More)
In this paper, we consider a method for computing the similarity of executable files, based on opcode graphs. We apply this technique to the challenging problem of metamorphic malware detection and compare the results to previous work based on hidden Markov models. In addition, we analyze the effect of various morphing techniques on the success of our(More)
Metamorphic malware changes its internal structure across generations, but its functionality remains unchanged. Well-designed metamorphic malware will evade signature detection. Recent research has revealed techniques based on hidden Markov models (HMMs) for detecting many types of metamorphic malware, as well as techniques for evading such detection. A(More)
Metamorphic malware changes its internal structure with each generation, while maintaining its original behavior. Current commercial antivirus software generally scan for known malware signatures; therefore, they are not able to detect metamorphic malware that sufficiently morphs its internal structure. Machine learning methods such as hidden Markov models(More)
comparison between our approach and commercial virus scanners. I would also like to thank my friends and schoolmates for their technical and emotional support. I want to thank Yue Wang for performing the virus scanning, and Peter Hey for repairing my hard disk after it crashed at the most critical moment. Finally I want to thank my family for their(More)
To evade signature-based detection, metamorphic viruses transform their code before each new infection. Software similarity measures are a potentially useful means of detecting such malware. We can compare a given file to a known sample of metamorphic malware and compute their similarity—if they are sufficiently similar, we classify the file as malware of(More)
Detection of malicious software (malware) continues to be a problem as hackers devise new ways to evade available methods. The proliferation of malware and malware variants requires methods that are both powerful, and fast to execute. This paper proposes a method to derive the common execution behavior of a family of malware instances. For each instance, a(More)