![]() ![]() ![]() Over 400 million new malware variants were identified that year alone. Symantec reported over 5 billion attacks in 2011, an 81% increase over 2010. If users do not have confidence that their machines will not be attacked when connected to the internet, major areas of computing will be constrained due to fear of denial of service and massive data fraud. All bioinformatics and data mining analysis were performed on publicly available tools and Weka. Moreover, aligned signature sequences can be mined through traditional data mining techniques to extract metasignatures that help to distinguish between viral and worm signatures. We show that, if malware signatures are represented as artificial protein sequences, it is possible to apply standard sequence alignment techniques in bioinformatics to improve accuracy of distinguishing between worm and virus signatures. The aim of this paper is to evaluate a static structure approach to malware modelling using the growing malware signature databases now available. ![]() ![]() Malware modelling has focused primarily on semantics due to the intended actions and behaviours of viral and worm code. The continuous growth of malware presents a problem for internet computing due to increasingly sophisticated techniques for disguising malicious code through mutation and the time required to identify signatures for use by antiviral software systems (AVS). ![]()
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