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In this article, a non-signature based statistical scanner for metamorphic malware detection, employing feature ranking methods like Term Frequency-Inverse Document Frequency-Class Frequency (TF-IDF-CF), Galavotti-Sebastiani-Simi Coefficient (GSS), Term Significance (TS) and Odds Ratio (OR) is proposed. Malware and benign models for classification are created by considering top ranked features obtained...
To unfold a solution for the detection of metamorphic viruses (obfuscated malware), we propose a non signature based approach using feature selection techniques such as Categorical Proportional Difference (CPD), Weight of Evidence of Text (WET), Term Frequency-Inverse Document Frequency (TF-IDF) and Term Frequency-Inverse Document Frequency-Class Frequency (TF-IDF-CF). Feature selection methods are...
Metamorphic malware modifies the code of every new offspring by using code obfuscation techniques. Recent research have depicted that metamorphic writers make use of benign dead code to thwart signature and Hidden Markov based detectors. Failure in the detection is due to the fact that the malware code appear statistically similar to benign programs. In order to detect complex malware generated with...
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