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The sophistication of novel strains of polymorphic viruses, such as Stuxnet, has increased over the last decade. Traditional tools such as anti-virus, firewalls, intrusion detection/prevention systems, etc. may be incapable of detecting such strains. As a result, new methods need to be introduced in order to detect this family of malware. Combining dynamic malware analysis techniques with machine...
In recent years, malicious software has affected and overshadowed personal computer and computer network securities. For this reason, searching for innovative solutions to detect malware has become increasingly important. In this paper, we develop a malware detection method using similarity measurement algorithms. The purpose of the proposed method is to improve the malware detection rate and detection...
Recent research work shows that feature fusion technique is not widely used in computer virus detection. Viruses generated from kits like NGVCK are detected effectively using feature fusion approach. Our purpose is to examine various flavours of feature fusion approach in virus detection.
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...
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