Metamorphic Malware
Metamorphic Malware
Related publications
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Using Markov Chains to Filter Machine-morphed
Variants of Malicious Programs,
Proceedings of the 3rd International Conference on Malicious and Unwanted Software (Malware'08) [to appear],
2008.
-
Metamorphic Authorship Recognition Using Markov Models,
Virus Bulletin,
2008.
-
The Design Space of Metamorphic Malware,
Proceedings of the 2nd International Conference on Information Warfare,
2007.
-
Using Engine Signature to Detect Metamorphic Malware,
Proceedings of the Fourth ACM Workshop on Rapid Malcode (WORM),
pp.73-78,
2006.
-
Are Metamorphic Viruses Really Invincible? Part 1,
Virus Bulletin,
pp.5-7,
2004.
-
Are Metamorphic Viruses Really Invincible? Part 2,
Virus Bulletin,
pp.9-12,
2005.
Metamorphic malware change as they reproduce or propagate, making
it difficult to find consistent patterns in the variants. This,
in turn, makes it challenging to recognize and stop the programs.
We are seeking to develop a theoretical understand various
classes of metamorphic malware, and to develop sound techniques
for managing metamorphism.
Research in this area includes:
- A better theoretical understanding of metamorphic programs
and their powers. We are presently
developing a classification system for malware that seeks to
organize them according to the theoretical powers each class
has for obfuscation; this is also expected to lead to better
threat models.
- Theory-guided methods for handling malware given the
understood threat models.
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Normalizing Malware: The "Unmorph" Project
One class of metamorphic programs are those that perform only
semantics-preserving transformations of their own code such that
they can be characterized by a conditional term rewriting system.
We have shown that once the metamorphic "engine" (i.e., transformation
engine) is modeled it is frequently possible to either automatically
or semi-automatically build a normalizer for that
engine.
These normalizers are proven to never create false positive or negative
matches. We have also shown that certain approximations may be
possible, making the normalization process much more efficient at
the cost of some false negatives. A prototype term-rewriting
normalizer was constructed using TXL,
and a case study illustrated the feasibility of the approach using
the W32/Evol worm as a subject.
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