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Towards Automatically Generating Double-Free Vulnerability Signatures Using Petri Nets

  • Ryan Iwahashi
  • Daniela A. S. de Oliveira
  • S. Felix Wu
  • Jedidiah R. Crandall
  • Young-Jun Heo
  • Jin-Tae Oh
  • Jong-Soo Jang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5222)

Abstract

With the increased popularity of polymorphic and register spring attacks, exploit signatures intrusion detection systems (IDS) can no longer rely only on exploit signatures. Vulnerability signatures that pattern match based on properties of the vulnerability instead of the exploit should be employed. Recent research has proposed three classes of vulnerability signatures but its approach cannot address complex vulnerabilities such as the ASN.1 Double-Free. Here we introduce Petri nets as a new class of vulnerability signature that could potentially be used to detect other types of vulnerabilities. Petri nets can be automatically generated and are represented as a graph making it easier to understand and debug. We analyzed it along side the three other classes of vulnerability signatures in relation to the Windows ASN.1 vulnerability. The results were very promising due to the very low false positive rate and 0% false negative rate. We have shown that Petri nets are a very efficient, concise, and effective way of describing signatures (both vulnerability and exploit). They are more powerful than regular expressions and still efficient enough to be practical. Comparing with the other classes, only Turing machines provided a better identification rate but they incur significant performance overhead.

Keywords

Turing Machine Regular Expression Intrusion Detection System Symbolic Execution Free List 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ryan Iwahashi
    • 1
  • Daniela A. S. de Oliveira
    • 1
  • S. Felix Wu
    • 1
  • Jedidiah R. Crandall
    • 2
  • Young-Jun Heo
    • 3
  • Jin-Tae Oh
    • 3
  • Jong-Soo Jang
    • 3
  1. 1.University of California at Davis 
  2. 2.University of New Mexico 
  3. 3.ETRI 

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