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Zero-day Malware Detection based on Supervised Learning Algorithms of API call Signatures

Alazab, Mamoun; Venkatraman, Sitalakshmi; Watters, Paul; Alazab, Moutaz

Description

Zero-day or unknown malware are created using code obfuscation techniques that can modify the parent code to produce offspring copies which have the same functionality but with different signatures. Current techniques reported in literature lack the capability of detecting zero-day malware with the required accuracy and efficiency. In this paper, we have proposed and evaluated a novel method of employing several data mining techniques to detect and classify zero-day malware with high levels of...[Show more]

CollectionsANU Research Publications
Date published: 2011
Type: Conference paper
URI: http://hdl.handle.net/1885/21347
Source: Proceedings of the 9th Australasian Data Mining Conference

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