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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...
The proposed non-signature based system creates a meta feature space for the detection of metamorphic malware samples where three sets of features are extracted from the files: (a) branch opcodes (b) unigrams (c) bigrams. The feature space is initially pruned using Naïve Bayes method. After the rare feature elimination process, the relevant opcodes that are highly contributing towards the target class...
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...
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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