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Polygraph: Automatically Generating Signatures for Polymorphic Worms

Published: 08 May 2005 Publication History

Abstract

It is widely believed that content-signature-based intrusion detection systems (IDSes) are easily evaded by polymorphic worms, which vary their payload on every infection attempt. In this paper, we present Polygraph, a signature generation system that successfully produces signatures that match polymorphic worms. Polygraph generates signatures that consist of multiple disjoint content substrings. In doing so, Polygraph leverages our insight that for a real-world exploit to function properly, multiple invariant substrings must often be present in all variants of a payload; these substrings typically correspond to protocol framing, return addresses, and in some cases, poorly obfuscated code. We contribute a definition of the polymorphic signature generation problem; propose classes of signature suited for matching polymorphic worm payloads; and present algorithms for automatic generation of signatures in these classes. Our evaluation of these algorithms on a range of polymorphic worms demonstrates that Polygraph produces signatures for polymorphic worms that exhibit low false negatives and false positives.

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  • (2023)Generative intrusion detection and prevention on data streamProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620479(4319-4335)Online publication date: 9-Aug-2023
  • (2023)HorusEyeProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620270(571-588)Online publication date: 9-Aug-2023
  • (2021)AutoComboProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481896(3777-3786)Online publication date: 26-Oct-2021
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Published In

cover image Guide Proceedings
SP '05: Proceedings of the 2005 IEEE Symposium on Security and Privacy
May 2005
244 pages
ISBN:0769523390

Publisher

IEEE Computer Society

United States

Publication History

Published: 08 May 2005

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Cited By

View all
  • (2023)Generative intrusion detection and prevention on data streamProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620479(4319-4335)Online publication date: 9-Aug-2023
  • (2023)HorusEyeProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620270(571-588)Online publication date: 9-Aug-2023
  • (2021)AutoComboProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3481896(3777-3786)Online publication date: 26-Oct-2021
  • (2021)A Survey of Binary Code SimilarityACM Computing Surveys10.1145/344637154:3(1-38)Online publication date: 17-Apr-2021
  • (2020)Automatic Yara Rule Generation Using BiclusteringProceedings of the 13th ACM Workshop on Artificial Intelligence and Security10.1145/3411508.3421372(71-82)Online publication date: 13-Nov-2020
  • (2019)Automated Pattern Inference Based on Repeatedly Observed Malware ArtifactsProceedings of the 14th International Conference on Availability, Reliability and Security10.1145/3339252.3340510(1-10)Online publication date: 26-Aug-2019
  • (2019)Zero-Day Signature Extraction for High-Volume AttacksIEEE/ACM Transactions on Networking10.1109/TNET.2019.289912427:2(691-706)Online publication date: 1-Apr-2019
  • (2019)Defeating denial-of-service attacks in a self-managing N-variant systemProceedings of the 14th International Symposium on Software Engineering for Adaptive and Self-Managing Systems10.1109/SEAMS.2019.00024(126-138)Online publication date: 25-May-2019
  • (2018)Hunting Observable Objects for Indication of CompromiseProceedings of the 13th International Conference on Availability, Reliability and Security10.1145/3230833.3233282(1-8)Online publication date: 27-Aug-2018
  • (2018)Efficient Repair of Polluted Machine Learning Systems via Causal UnlearningProceedings of the 2018 on Asia Conference on Computer and Communications Security10.1145/3196494.3196517(735-747)Online publication date: 29-May-2018
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