Network Security White Papers
Embedded Malware Detection Using Markov n-grams
Overview Embedded malware is a recently discovered security threat that allows malcode to be hidden inside a benign file. It has been shown that embedded malware is not detected by commercial antivirus software even when the malware signature is present in the antivirus database. This paper presents a novel anomaly detection scheme to detect embedded malware. They first analyze byte sequences in benign files to show that benign files' data generally exhibit a 1-st order dependence structure. Consequently, conditional n-grams provide a more meaningful representation of a file's statistical properties than traditional n-grams. To capture and leverage this correlation structure for embedded malware detection, they model the conditional distributions as Markov n-grams.
| Publisher | NUST Institute of Information Technology | File Format | |
|---|---|---|---|
| Date Published | February 2008 | ||
| Format | White Papers | ||
| Topics | |||
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