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Volumn 5048 LNCS, Issue , 2008, Pages 299-304

Detecting communication protocol security flaws by formal fuzz testing and machine learning

Author keywords

Fuzz testing; Protocol Synthesis; Security Testing

Indexed keywords

FORMAL METHODS; MACHINE LEARNING; NETWORK PROTOCOLS; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; LOGIC CIRCUITS; MOBILE SECURITY;

EID: 45849102072     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-68855-6_19     Document Type: Conference Paper
Times cited : (23)

References (12)
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  • 5
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    • Godefroid, P., Levin, M.Y., Molnar, D.: Automated Whitebox Fuzz Testing. Technical Report MS-TR-2007-58, Microsoft (May 2007)
    • Godefroid, P., Levin, M.Y., Molnar, D.: Automated Whitebox Fuzz Testing. Technical Report MS-TR-2007-58, Microsoft (May 2007)
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    • On state reduction of incompletely specified finite state machines
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    • Gören, S.1    Ferguson, F.J.2
  • 7
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    • Inside the Windows Security Push
    • Howard, M.: Inside the Windows Security Push. IEEE Security & Privacy, 57-61 (2003)
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    • Howard, M.1
  • 8
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  • 9
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    • Violating Assumptions with Fuzzing
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    • Oehlert, P.1
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    • Testing Security Properties of protocol implementations - a machine learning based approach
    • Shu, G., Lee, D.: Testing Security Properties of protocol implementations - a machine learning based approach. In: Proceedings of IEEE ICDCS (2007)
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.