Download Detection of Intrusions and Malware, and Vulnerability by Sven Dietrich PDF

By Sven Dietrich

This publication constitutes the refereed court cases of the eleventh foreign convention on Detection of Intrusions and Malware, and Vulnerability evaluate, DIMVA 2014, held in Egham, united kingdom, in July 2014.

The thirteen revised complete papers awarded including one prolonged summary have been rigorously reviewed and chosen from 60 submissions. The papers are prepared in topical sections on malware, cellular defense, community safeguard and host security.

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Read Online or Download Detection of Intrusions and Malware, and Vulnerability Assessment: 11th International Conference, DIMVA 2014, Egham, UK, July 10-11, 2014. Proceedings PDF

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Extra info for Detection of Intrusions and Malware, and Vulnerability Assessment: 11th International Conference, DIMVA 2014, Egham, UK, July 10-11, 2014. Proceedings

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Classification of packed executables for accurate computer virus detection. Pattern Recognition Letters 29(14), 1941–1946 (2008) 26. : Malware analysis and attribution using genetic information. In: 2012 7th International Conference on Malicious and Unwanted Software (MALWARE), pp. 39–45. IEEE (2012) 27. : Mining of Massive Datasets. Cambridge University Press (2012) 28. : Unpacking virtualization obfuscators. In: Proceedings of the 3rd USENIX Conference on Offensive Technologies, p. 1. USENIX Association (2009) 29.

RAID 2005. LNCS, vol. 3858, pp. 207–226. Springer, Heidelberg (2006) 18. : Fast location of similar code fragments using semantic ‘juice’. In: SIGPLAN Program Protection and Reverse Engineering Workshop, p. 5. ACM (2013) 19. : Vilo: a rapid learning nearestneighbor classifier for malware triage. Journal of Computer Virology and Hacking Techniques, 1–15 (2013) 20. : Function extraction (FX) research for computation of software behavior: 2010 development and application of semantic reduction theorems for behavior analysis.

This also defeats automatic trigger-based malware detection techniques which rely on these static and dynamic analysis primitives. The hidden code may be a backdoor, or implement trigger-based botnet behavior, similar to that found in the Gauss malware [7]. In addition, it is possible to hide kernel-level or user-level rootkits even from detectors outside the compromised environment. Our technique hides malicious code at the binary level, by encoding it in unaligned instructions which are contained within a spurious instruction stream [10].

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