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      RNN을 이용한 코드 재사용 공격 탐지 방법 연구 = Detecting code reuse attack using RNN

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      https://www.riss.kr/link?id=A105456271

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      다국어 초록 (Multilingual Abstract)

      A code reuse attack is an attack technique that can execute arbitrary code without injecting code directly into the stack by combining executable code fragments existing in program memory and executing them continuously. ROP(Return-Oriented Programming) attack is typical type of code reuse attack and serveral defense techniques have been proposed to deal with this. However, since existing methods use Rule-based method to detect attacks based on specific rules, there is a limitation that ROP attacks that do not correspond to previously defined rules can not be detected. In this paper, we introduce a method to detect ROP attack by learning command pattern used in ROP attack code using RNN(Recurrent Neural Network). We also show that the proposed method effectively detects ROP attacks by measuring False Positive Ratio, False Negative Ratio, and Accuracy for normal code and ROP attack code discrimination.
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      A code reuse attack is an attack technique that can execute arbitrary code without injecting code directly into the stack by combining executable code fragments existing in program memory and executing them continuously. ROP(Return-Oriented Programmin...

      A code reuse attack is an attack technique that can execute arbitrary code without injecting code directly into the stack by combining executable code fragments existing in program memory and executing them continuously. ROP(Return-Oriented Programming) attack is typical type of code reuse attack and serveral defense techniques have been proposed to deal with this. However, since existing methods use Rule-based method to detect attacks based on specific rules, there is a limitation that ROP attacks that do not correspond to previously defined rules can not be detected. In this paper, we introduce a method to detect ROP attack by learning command pattern used in ROP attack code using RNN(Recurrent Neural Network). We also show that the proposed method effectively detects ROP attacks by measuring False Positive Ratio, False Negative Ratio, and Accuracy for normal code and ROP attack code discrimination.

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      참고문헌 (Reference)

      1 H.Shacham, "The Geometry of Innocent Flesh on the Bone: Return-into-libc without Function Calls (on the x86)" 552-556, 2007

      2 K. Yao, "Spoken Language Understanding Using Long Short-Term Memory Neural Network" 2014

      3 P.Bania, "Security Mitigations for Return-Oriented Programming Attacks"

      4 M. Kayaalp, "Scrap: Architecture for signature-based protection from code reuse attacks" 2013

      5 K. Yao, "Recurrent Conditional Random Field For Language Understanding" 2014

      6 CK. Luk, "Pin: Building customized program analysis tools with dynamic instrumentation" 190-200, 2005

      7 S. Hochreiter, "Long Short-Term Memory" 1997

      8 K. Cho, "Learning phrase representations using RNN encoder-decoder for statistical machine translation" 1724-1734, 2014

      9 K. Onarlioglu, "G-free: Defeating return-oriented programming through gadget-less binaries" 49-58, 2010

      10 J. Li, "Defeating return-oriented rootkits with return-less kernels" 2010

      1 H.Shacham, "The Geometry of Innocent Flesh on the Bone: Return-into-libc without Function Calls (on the x86)" 552-556, 2007

      2 K. Yao, "Spoken Language Understanding Using Long Short-Term Memory Neural Network" 2014

      3 P.Bania, "Security Mitigations for Return-Oriented Programming Attacks"

      4 M. Kayaalp, "Scrap: Architecture for signature-based protection from code reuse attacks" 2013

      5 K. Yao, "Recurrent Conditional Random Field For Language Understanding" 2014

      6 CK. Luk, "Pin: Building customized program analysis tools with dynamic instrumentation" 190-200, 2005

      7 S. Hochreiter, "Long Short-Term Memory" 1997

      8 K. Cho, "Learning phrase representations using RNN encoder-decoder for statistical machine translation" 1724-1734, 2014

      9 K. Onarlioglu, "G-free: Defeating return-oriented programming through gadget-less binaries" 49-58, 2010

      10 J. Li, "Defeating return-oriented rootkits with return-less kernels" 2010

      11 Microsoft, "Data Execution Prevention(DEP)"

      12 P. Chen, "DROP: Detecting return-oriented programming malicious code" LNCS 5905 : 163-177, 2009

      13 P. J. Werbos, "Backpropagation through time: what it does and how to do it" 1990

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2027 평가예정 재인증평가 신청대상 (재인증)
      2021-01-01 평가 등재학술지 유지 (재인증) KCI등재
      2018-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2015-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2013-11-05 학술지명변경 외국어명 : Journal of Korean Society for Internet Information -> Journal of Internet Computing and Services KCI등재
      2011-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2009-01-01 평가 등재학술지 유지 (등재유지) KCI등재
      2006-01-01 평가 등재학술지 선정 (등재후보2차) KCI등재
      2005-01-01 평가 등재후보 1차 PASS (등재후보1차) KCI등재후보
      2003-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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      학술지 인용정보

      학술지 인용정보
      기준연도 WOS-KCI 통합IF(2년) KCIF(2년) KCIF(3년)
      2016 0.55 0.55 0.63
      KCIF(4년) KCIF(5년) 중심성지수(3년) 즉시성지수
      0.64 0.6 0.85 0.03
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