E Commerce

Detection of Intrusions and Malware, and Vulnerability by Sven Dietrich

By Sven Dietrich

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

The thirteen revised complete papers offered including one prolonged summary have been rigorously reviewed and chosen from 60 submissions. The papers are prepared in topical sections on malware, cellular safety, community defense 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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We develop a general architecture for identifying shared components in a corpus using a two-stage clustering technique. While our method is parametrized on any features extracted from a binary, our implementation uses features abstracting the semantics of blocks of instructions. Our system has been found to identify shared components with extremely high accuracy in a rigorous, controlled experiment conducted independently by MITLL. Our technique provides an automated method to find between malware code functional relationships that may be used to establish evolutionary relationships and aid in forensics.

Therefore, after we have identified the components in the malware using our algorithm, we are provided with the virtual address byte labels of the |T| components in |T| different samples (the byte locations on the rest of the samples are not provided; those are only used during evaluation by the sponsoring agency). We then create a mapping from our discovered components to the revealed components by greedily assigning the best match of our components to the revealed ones, where multiple discovered components can be assigned to a single revealed component.

TC1 contains 50 samples of malware and eight components. , optimizations on or off) to produce a data set of 250 malware samples. Finally, TC3 contained 27 total components over 500 malware samples, where 250 of the malware samples are the same ones from TC2. Note that in all tests, the algorithms do not have prior knowledge of the number of components in the data set. For the K-means tests, we set a reasonable upper bound on the estimated number of components. For IV&V we used K = 50. 2 Quality Metrics The quality metrics employed are motivated by MITLL’s testing methodology.

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