2,743 research outputs found
Malware Detection Using Dynamic Analysis
In this research, we explore the field of dynamic analysis which has shown promis- ing results in the field of malware detection. Here, we extract dynamic software birth- marks during malware execution and apply machine learning based detection tech- niques to the resulting feature set. Specifically, we consider Hidden Markov Models and Profile Hidden Markov Models. To determine the effectiveness of this dynamic analysis approach, we compare our detection results to the results obtained by using static analysis. We show that in some cases, significantly stronger results can be obtained using our dynamic approach
Malicious cryptography techniques for unreversable (malicious or not) binaries
Fighting against computer malware require a mandatory step of reverse
engineering. As soon as the code has been disassemblied/decompiled (including a
dynamic analysis step), there is a hope to understand what the malware actually
does and to implement a detection mean. This also applies to protection of
software whenever one wishes to analyze them. In this paper, we show how to
amour code in such a way that reserse engineering techniques (static and
dymanic) are absolutely impossible by combining malicious cryptography
techniques developped in our laboratory and new types of programming (k-ary
codes). Suitable encryption algorithms combined with new cryptanalytic
approaches to ease the protection of (malicious or not) binaries, enable to
provide both total code armouring and large scale polymorphic features at the
same time. A simple 400 Kb of executable code enables to produce a binary code
and around mutated forms natively while going far beyond the old
concept of decryptor.Comment: 17 pages, 2 figures, accepted for presentation at H2HC'1
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