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武汉大学学报 英文版 | Wuhan University Journal of Natural Sciences
Wan Fang
Wuhan University
Latest Article
A Vulnerability Model Construction Method Based on Chemical Abstract Machine
LI Xiang, CHEN Jinfu, LIN Zhechao, ZHANG Lin, WANG Zibin, ZHOU Minmin, XIE Wanggen
1. National Key Laboratory of Science and Technology on Information System Security, Beijing 100101, China; 2. Beijing Institute of System Engineering, Beijing 100101, China; 3. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang 212013, Jiangsu, China
It is difficult to formalize the causes of vulnerability, and there is no effective model to reveal the causes and characteristics of vulnerability. In this paper, a vulnerability model construction method is proposed to realize the description of vulnerability attribute and the construction of a vulnerability model. A vulnerability model based on chemical abstract machine(CHAM) is constructed to realize the CHAM description of vulnerability model, and the framework of vulnerability model is also discussed. Case study is carried out to verify the feasibility and effectiveness of the proposed model. In addition, a prototype system is also designed and implemented based on the proposed vulnerability model. Experimental results show that the proposed model is more effective than other methods in the detection of software vulnerabilities.
Key words:software security, vulnerability detection; vulnerability analysis; vulnerability model; chemical abstract machine
CLC number:TP 309
[1]	CVND. 2016 CNVD Vulnerability Data Statistics Briefing [EB/OL].[2017-04-12]. http://www.cnvd.org.cn/webinfo/show/ 40-40.
[2]	Aslam T, Krsul I. Use of a taxonomy of security faults. eugene spafford [C]// Proceedings of the 19th National Information Systems Security Conference. Baltimore: Purdue University, 1996: 551-560.
[3]	Krsul I.Software Vulnerability Analysis[R].West Lafayette: Department of Computer Sciences, Purdue University, 1998, 23 (3): 25-36.
[4]	Li P, Cui B. A comparative study on software vulnerability static analysis techniques and tools[C]// IEEE International Conference on Information Theory and Information Security. Washington D C: IEEE , 2010: 521-524.
[5]	Cadariu M, Bouwers E, Visser J, et al. Tracking known security vulnerabilities in proprietary software systems[C]// International Conference on Software Analysis, Evolution and Reengineering. Washington D C: IEEE Computer Society, 2015: 516-519.
[6]	Zhang S, Caragea D, Ou X. An empirical study on using the national vulnerability database to predict software vulnerabilities[C]// International Conference on Database and Expert Systems Applications. Berlin: Springer-Verlag, 2011, 6860: 217-231.
[7]	Anand P. Overview of root causes of software vulnera- bilities-technical and user-side perspectives[C]// Internat- ional Conference on Software Security and Assurance (ICSSA). Washington D C: IEEE, 2016: 70-74.
[8]	Scholte T, Balzarotti D, Kirda E. Have things changed now? An empirical study on input validation vulnerabilities in web applications[J]. Computers & Security, 2012, 31: 344-356.
[9]	Tang Y, Zhao F, Yang Y, et al. Predicting vulnerable components via text mining or software metrics? An effort- aware perspective[C]// IEEE International Conference on Software Quality, Reliability and Security (QRS). Washington D C: IEEE, 2015: 27-36.
[10]	Kapur P, Yadavali V S, Shrivastava A. A comparative study of vulnerability discovery modeling and software reliability growth modeling[C]// International Conference on Futuristic Trends on Computational Analysis and Knowledge Manage- ment (ABLAZE), 2015: 246-251.
[11]	Li H, Kim T, Bat-Erdene M, et al. Software vulnerability detection using backward trace analysis and symbolic execution[C]// International Conference on Availability, Reliability and Security. Washington D C: IEEE Computer Society, 2013, 6(3): 446-454.
[12]	Younis A A, Malaiya Y K, Ray I. Using attack surface entry points and reachability analysis to assess the risk of software vulnerability exploitability[C]// IEEE International Symposium on High-Assurance Systems Engineering. Washington D C: IEEE Computer Society, 2014: 1-8.
[13]	Anand A, Bhatt N. Vulnerability discovery modeling and weighted criteria based ranking[J]. Journal of the Indian Society for Probability and Statistics, 2016, 17(1):1-10. 
[14]	Wang T, Han L, Fu C, et al. Software vulnerability static detection model and detection framework [J]. Computer Science, 2016, 43 (5): 80-86 (Ch).
[15]	Chen J F, Chen J M, Huang R B, et al. An approach of security testing for third-party component based on state mutation[J]. Security and Communication Networks (SCN), 2016, 9(15): 2827-2842. 
[16]	Tang C L, Dong J Q, Dai D B, et al. A similarity query algorithm for sequence pattern[J]. Computer Research and Development, 2011: 132-139 ( Ch).
[17]	Chen J F, Zhu L L, Xie Z B, et al. An effective long string searching algorithm towards component security testing[J]. China Communications, 2016, 13(11): 153-169.
[18]	Yamaguchi F, Golde N, Arp D, et al. Modeling and discovering vulnerabilities with code property graphs[C]// 2014 IEEE Symposium on Security and Privacy (SP). Washington D C : IEEE, 2014: 590-604.
[19]	Singh D, Choudhary J P, De M. An effort to select a preferable metaheuristic model for knowledge discovery in data mining[J]. Inderscience Publishers, 2015, 4(1): 57-90.
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