期刊文献+

基于系统调用的软件行为模型 被引量:15

Software Behavior Model Based on System Calls
下载PDF
导出
摘要 由于系统调用信息可以在一定程度上反映程序的行为特性,因此利用系统调用来对程序行为进行建模是目前入侵检测领域的研究热点。以静态建模、动态建模和混合建模这3种不同的建模方式为切入点,按照时间顺序将基于系统调用的软件行为模型的发展划分为3个阶段:初期阶段、发展阶段和综合发展阶段。然后剖析了各阶段内的模型的发展轨迹以及它们之间的内在联系,并对它们做了横向对比分析。研究表明,基于系统调用的软件行为建模技术的发展趋势应是结合静态和动态建模技术以及结合系统调用的控制流信息和数据流信息,并综合考虑其他实时信息,如环境变量和上下文信息等,开发出检测能力更强、完备性更高以及实际可行性高的软件行为模型。 Modeling program behavior based on system call has become the hot topic in intrusion detection since system call can reflect the program behavior in some degree. This paper studied three different types of modeling methods that are dynamically modeling, statically modeling and hybridly modeling as the breakthrough point, and concluded that the development process of behavior models can be divided into three stages: initial stage, developmental stage and synthetical stage. The evaluation and comparison experiments were done to find the inherent relations and development track of some typical models in different stages. The whole analysis in this paper indicates that the future trend of behavior modeling methods is to develop a software behavior model with high detection capability, completeness, and actual feasibility through the combination consideration of the static techniques with dynamic techniques, the control flow with data flow,and the other real-time information such as environment variables and context information.
出处 《计算机科学》 CSCD 北大核心 2010年第4期151-157,共7页 Computer Science
基金 863国家重点基金项目(2007AA01Z411) 国家自然科学基金(90718005)资助
关键词 行为模型 入侵检测 系统调用 Behavior model, Intrusion detection, System call
  • 相关文献

参考文献44

  • 1Forrest S.A sense of self for UNIX processes[C]//Proc.of the IEEE Symp.on Security and Privacy.Oakland,IEEE Press,1996:120-128.http://www.cs.unm.edu// forrest/publica-tions/ieee-sp-96-unix.pdf.
  • 2Hofmeyr S A,Forrest S,Somayaji A.Intrusion detection using sequences of system calls[J].Journal of Computer Security,1998,6(3):151-180.
  • 3Ramkumar C,van den BE.A Fast Static Analysis Approach to Detect Exploit Code Inside Network Flows[C]//Proceedings of the International Symposium on Recent Advances in Intrusion Detection (RAID).Berlin:Springer Verlag,2006,3858:284-308.
  • 4Liu Zhen,Bridges S M,Vaughn R R Combining Static Analysis and Dynamic Learning to Build Accurate Intrusion Detection Models[C]//IEEE International Information Assurance Workshop 2005.Washington D C:IEEE Computer Society Press,2005:164-177.
  • 5Giffin J T,Jha S,Miller B P.Detecting manipulated remote call streams[C]//Proc.of the 11th USENIX Security Symp.San Francisco:USENIX,2002:61-79.
  • 6Gao Debin,Reiter M K,Song Dawn.Behavioral Distance for Intrusion Detection[C]//Proceedings of the 8th International Symposium on Recent Advances in Intrusion Detection (RAID 2005).Seattle,WA,USA,September 2005.
  • 7Gao Debin,Reiter M K,Song Dawn.Behavioral Distance Measurement Using Hidden Markov Models[C]//Proceedings of the 9th International Symposium on Recent Advances in Intrusion Detection (RAID 2006).Hamburg,Germany,September 2006.
  • 8Gao Debin,Reiter M K,Song Dawn.Beyond Output Voting:Detecting Compromised Replicas Using Behavioral Distance[R],CMU-CYLAB-06-019.December 2006.
  • 9Gao D,Reiter M K.Song D.On gray-box program tracking for anomaly detection[C]//USENIX Security Symposium.SanDiego,California,August 2004.
  • 10姚立红,訾小超,黄皓,茅兵,谢立.基于系统调用特征的入侵检测研究[J].电子学报,2003,31(8):1134-1137. 被引量:17

二级参考文献201

  • 1张相锋,孙玉芳,赵庆松.基于系统调用子集的入侵检测[J].电子学报,2004,32(8):1338-1341. 被引量:10
  • 2ZHONG An-ming 1, JIA Chun-fu 1,21.College of Information Technology and Sciences, Nankai University, Tianjin 300071,China,2.State Key Laboratory of Information Security, Institute of Software of Chinese Academy of Science, Beijing 100039,China.Privilege Flow Oriented Intrusion Detection Based on Hidden Semi-MarkovModel[J].Wuhan University Journal of Natural Sciences,2005,10(1):137-141. 被引量:2
  • 3钟安鸣,贾春福.基于系统调用入侵检测的马氏链模型[J].计算机应用研究,2005,22(4):134-136. 被引量:3
  • 4苏璞睿,杨轶.基于可执行文件静态分析的入侵检测模型[J].计算机学报,2006,29(9):1572-1578. 被引量:14
  • 5Forrest S., Hofmeyr S.A., Somayaji A., Longstaff T.A.. A sense of self for Unix processes. In: Proceedings of the 1996 IEEE Symposium on Security and Privacy. Los Alamitos, CA: IEEE Computer Society Press, 1996, 120~128
  • 6Lee Wenke, Xiang Dong. Information-theoretic measures for anomaly detection. In: Proceedings of the 2001 IEEE Symposium on Security and Privacy, Oakland, California, USA, 2001, 130~143
  • 7Lane T., Brodley C.E.. Temporal sequence learning and data reduction for anomaly detection. In: Proceedings of the 5th ACM Conference on Computer & Communication Security, San Francisco, California, USA, 1998, 295~331
  • 8Goldman R.P., Geib C.W., Miller C.A.. A new model of plan recognition. In: Proceedings of the 1999 Conference on Uncertainty in Artificial Intelligence, Stockholm, Sweden, 1999, 245~254
  • 9Charniak E., Goldman R.. A Bayesian model of plan recognition. Artificial Intelligence, Elsevier Science Publishers. 1993, 64(1): 53~79
  • 10Albrecht R., Zukerman R., Nicholson A., Bud A.. Towards a Bayesian model for keyhole plan recognition in large domains. In: Proceedings of the 6th International Conference on User Modeling. Sardinia, Italy, 1997, 365~376

共引文献384

同被引文献116

引证文献15

二级引证文献36

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部