IEICE TRANSACTIONS on Information
Binary Oriented Vulnerability Analyzer Based on Hidden Markov Model
Summary :
The letter proposes a novel binary vulnerability analyzer for executable programs that is based on the Hidden Markov Model. A vulnerability instruction library (VIL) is primarily constructed by collecting binary frames located by double precision analysis. Executable programs are then converted into structurized code sequences with the VIL. The code sequences are essentially context-sensitive, which can be modeled by Hidden Markov Model (HMM). Finally, the HMM based vulnerability analyzer is built to recognize potential vulnerabilities of executable programs. Experimental results show the proposed approach achieves lower false positive/negative rate than latest static analyzers.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.12 pp.3410-3413
- Publication Date
- 2010/12/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.3410
- Type of Manuscript
- LETTER
- Category
- Dependable Computing
Authors
Keyword
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Hao BAI, Chang-zhen HU, Gang ZHANG, Xiao-chuan JING, Ning LI, "Binary Oriented Vulnerability Analyzer Based on Hidden Markov Model" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 12, pp. 3410-3413, December 2010, doi: 10.1587/transinf.E93.D.3410.
Abstract: The letter proposes a novel binary vulnerability analyzer for executable programs that is based on the Hidden Markov Model. A vulnerability instruction library (VIL) is primarily constructed by collecting binary frames located by double precision analysis. Executable programs are then converted into structurized code sequences with the VIL. The code sequences are essentially context-sensitive, which can be modeled by Hidden Markov Model (HMM). Finally, the HMM based vulnerability analyzer is built to recognize potential vulnerabilities of executable programs. Experimental results show the proposed approach achieves lower false positive/negative rate than latest static analyzers.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.3410/_p
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@ARTICLE{e93-d_12_3410,
author={Hao BAI, Chang-zhen HU, Gang ZHANG, Xiao-chuan JING, Ning LI, },
journal={IEICE TRANSACTIONS on Information},
title={Binary Oriented Vulnerability Analyzer Based on Hidden Markov Model},
year={2010},
volume={E93-D},
number={12},
pages={3410-3413},
abstract={The letter proposes a novel binary vulnerability analyzer for executable programs that is based on the Hidden Markov Model. A vulnerability instruction library (VIL) is primarily constructed by collecting binary frames located by double precision analysis. Executable programs are then converted into structurized code sequences with the VIL. The code sequences are essentially context-sensitive, which can be modeled by Hidden Markov Model (HMM). Finally, the HMM based vulnerability analyzer is built to recognize potential vulnerabilities of executable programs. Experimental results show the proposed approach achieves lower false positive/negative rate than latest static analyzers.},
keywords={},
doi={10.1587/transinf.E93.D.3410},
ISSN={1745-1361},
month={December},}
Copy
TY - JOUR
TI - Binary Oriented Vulnerability Analyzer Based on Hidden Markov Model
T2 - IEICE TRANSACTIONS on Information
SP - 3410
EP - 3413
AU - Hao BAI
AU - Chang-zhen HU
AU - Gang ZHANG
AU - Xiao-chuan JING
AU - Ning LI
PY - 2010
DO - 10.1587/transinf.E93.D.3410
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2010
AB - The letter proposes a novel binary vulnerability analyzer for executable programs that is based on the Hidden Markov Model. A vulnerability instruction library (VIL) is primarily constructed by collecting binary frames located by double precision analysis. Executable programs are then converted into structurized code sequences with the VIL. The code sequences are essentially context-sensitive, which can be modeled by Hidden Markov Model (HMM). Finally, the HMM based vulnerability analyzer is built to recognize potential vulnerabilities of executable programs. Experimental results show the proposed approach achieves lower false positive/negative rate than latest static analyzers.
ER -
English
