On the infeasibility of modeling polymorphic shellcode. Re-thinking the role of learning in intrusion detection systems

DOI10.1007/s10994-009-5143-5zbMath1470.68038OpenAlexW1739467010WikidataQ114904892 ScholiaQ114904892MaRDI QIDQ1959622

Angelos D. Keromytis, Yingbo Song, Angelos Stavrou, Michael E. Locasto, Salvatore J. Stolfo

Publication date: 7 October 2010

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-009-5143-5

zbMATH Keywords

polymorphismblendingmetricsshellcode

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Computer security (68M25)

Related Items (1)

On the infeasibility of modeling polymorphic shellcode. Re-thinking the role of learning in intrusion detection systems

Uses Software

• TCPDUMP
• Valgrind
• Snort
• Autograph
• Polygraph

Cites Work

• On the infeasibility of modeling polymorphic shellcode. Re-thinking the role of learning in intrusion detection systems
• Reliable identification of bounded-length viruses is NP-complete
• Information Security
• Unnamed Item
• Unnamed Item

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