Zero-day Malware Detection based on Supervised Learning Algorithms of API call Signatures
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]
|Collections||ANU Research Publications|
|Source:||Proceedings of the 9th Australasian Data Mining Conference|
|01_Alazab_Zero-day_Malware_Detection_2011.pdf||280.43 kB||Adobe PDF||Request a copy|
|02_Alazab_Zero-day_Malware_Detection_2011.pdf||655.21 kB||Adobe PDF||Request a copy|
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