Malware variant detection.
Doctoral thesis, UCL (University College London).
Available under License : See the attached licence file.
Malware programs (e.g., viruses, worms, Trojans, etc.) are a worldwide epidemic. Studies and statistics show that the impact of malware is getting worse. Malware detectors are the primary tools in the defence against malware. Most commercial anti-malware scanners maintain a database of malware patterns and heuristic signatures for detecting malicious programs within a computer system. Malware writers use semantic-preserving code transformation (obfuscation) techniques to produce new stealth variants of their malware programs. Malware variants are hard to detect with today's detection technologies as these tools rely mostly on syntactic properties and ignore the semantics of malicious executable programs. A robust malware detection technique is required to handle this emerging security threat. In this thesis, we propose a new methodology that overcomes the drawback of existing malware detection methods by analysing the semantics of known malicious code. The methodology consists of three major analysis techniques: the development of a semantic signature, slicing analysis and test data generation analysis. The core element in this approach is to specify an approximation for malware code semantics and to produce signatures for identifying, possibly obfuscated but semantically equivalent, variants of a sample of malware. A semantic signature consists of a program test input and semantic traces of a known malware code. The key challenge in developing our semantics-based approach to malware variant detection is to achieve a balance between improving the detection rate (i.e. matching semantic traces) and performance, with or without the e ects of obfuscation on malware variants. We develop slicing analysis to improve the construction of semantic signatures. We back our trace-slicing method with a theoretical result that shows the notion of correctness of the slicer. A proof-of-concept implementation of our malware detector demonstrates that the semantics-based analysis approach could improve current detection tools and make the task more di cult for malware authors. Another important part of this thesis is exploring program semantics for the selection of a suitable part of the semantic signature, for which we provide two new theoretical results. In particular, this dissertation includes a test data generation method that works for binary executables and the notion of correctness of the method.
|Title:||Malware variant detection|
|Open access status:||An open access version is available from UCL Discovery|
|UCL classification:||UCL > School of BEAMS > Faculty of Engineering Science > Computer Science|
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