期刊文献+

Immune based computer virus detection approaches 被引量:1

Immune based computer virus detection approaches
下载PDF
导出
摘要 The computer virus is considered one of the most horrifying threats to the security of computer systems worldwide.The rapid development of evasion techniques used in virus causes the signature based computer virus detection techniques to be ineffective.Many novel computer virus detection approaches have been proposed in the past to cope with the ineffectiveness,mainly classified into three categories: static,dynamic and heuristics techniques.As the natural similarities between the biological immune system(BIS),computer security system(CSS),and the artificial immune system(AIS) were all developed as a new prototype in the community of anti-virus research.The immune mechanisms in the BIS provide the opportunities to construct computer virus detection models that are robust and adaptive with the ability to detect unseen viruses.In this paper,a variety of classic computer virus detection approaches were introduced and reviewed based on the background knowledge of the computer virus history.Next,a variety of immune based computer virus detection approaches were also discussed in detail.Promising experimental results suggest that the immune based computer virus detection approaches were able to detect new variants and unseen viruses at lower false positive rates,which have paved a new way for the anti-virus research. The computer virus is considered one of the most horrifying threats to the security of computer systems worldwide. The rapid development of evasion techniques used in virus causes the signature based computer virus detection techniques to be ineffective. Many novel computer virus detection approaches have been proposed in the past to cope with the ineffectiveness, mainly classified into three categories: static, dynamic and heuristics techniques. As the natural similarities between the biological immune sys- tem (BIS), computer security system (CSS), and the artificial immune system (AIS) were all developed as a new prototype in the community of anti-virus research. The immune mechanisms in the BIS provide the opportunities to construct computer virus detection models that are robust and adaptive with the ability to detect unseen viruses. In this paper, a variety of classic computer virus detection approaches were intro- duced and reviewed based on the background knowledge of the computer virus history. Next, a variety of immune based computer virus detection approaches were also discussed in detail. Promising experimental results suggest that the immune based computer virus detection approaches were able to detect new variants and unseen viruses at lower false positive rates, which have paved a new way for the anti-virus research.
出处 《智能系统学报》 CSCD 北大核心 2013年第1期80-94,共15页 CAAI Transactions on Intelligent Systems
基金 National Natural Science Foundation of China(No.61170057,60875080)
关键词 数据挖掘 计算机技术 发展现状 人工智能 computer virus detection artificial immune system immune algorithms hierarchical model negative selection algorithm with penalty factor
  • 相关文献

参考文献67

  • 1BAILEY M, OBERHEIDE J, ANDERSEN J, et al. Automated classification and analysis of internet malwm'e[ C]//The 10th Symposium on Recent Advances in Intrusion Detection. Gold Coast, Australia, 2007: 178-197.
  • 2PERELSON A S, WEISBUCH G. Immunology for physicists [J]. Reviews of Modem Physics, 1997, 69(4): 1219- 1268.
  • 3FORREST S, PERELSON A S, ALLEN L, et al. Self-non- self discrimination in a computer [ C ]//IEEE Computer So- ciety Symposium on Research in Security and Privacy. Oak- land, USA, 1994: 202-212.
  • 4KEPHART J O, ARNOLD W C. Automatic extraction of com- puter virus signatures[ C]//The 4th Virus Bulletin Internation- al Conference. Jersey Islands, UK, 1994: 178-184.
  • 5KEPHART J O, SORKIN G B, SWIMMER M, et al. Blue- print for a computer immune system [ C ]//Proceedings of the Seventh International Virus Bulletin Conference. San Francisco, USA, 1997: 159-173.
  • 6OKAMOTO T, ISH~DA Y. Distributed approach against com- puter viruses inspired by the immune system[J]. IEICE Trans- actions on Communications, 2000, 83(5) : 908-915.
  • 7WANG Wei, ZHANG Pengtao, TAN Ying, et al. A hierar- chical artificial immune model for virus detection [ C ]//In- ternational Conference on Computational Intelligence and Se- curity. Beijing, China, 2009: 1-5.
  • 8CHAO Rui, TAN Ying. A virus detection system based on artificial immune system [ C ]//International Conference on Computational Intelligence and Security. Beijing, China, 2009 : 6-10.
  • 9WANG Wei, ZHANG Pengtao, TAN Ying. An immune concentration based virus detection approach using particle swarm optimization [ C ]//International Conference on Swarm Intelligence. Beijing, China, 2010: 347-354.
  • 10COHEN F. Computer viruses : theory and experiments[ J ]. Computers and Security, 1987, 6(1) : 22-35.

二级参考文献11

  • 1Tabish S M,Shafiq M Z,Farooq M.Malware detection using statistical analysis of byte-level file content[].CSI- KDD’.2009
  • 2Ye Y F,Jiang Q S,Zhuang W W.Associative classification and post-processing techniques used for malware detection[].nd International Conference on Anti-counterfeiting Security and Identification.2008
  • 3Karnik A,Goswami S,Guha P.Detecting obfuscated viruses using cosine similarity analysis[].Proceedings of the First Asia International Conference on Modeling & Simulation.2007
  • 4Lee H,Kim W,Hong M.Artificial immune system against viral attack[].ICCS.2004
  • 5Edge K S,Lamont G B,Raines R A.A retrovirus inspired algorithm for virus detection & optimization[].Proceedings of the th Annual Conference on Genetic and Evolutionary Computation.2006
  • 6Li Z,Liang Y W,Wu Z J, et al.Immunity based virus detection with process call arguments and user feedback[].Bio-Inspired Models of Network Information and Computing Systems.2007
  • 7Balachandran S,Dasgupta D,Nino F, et al.A general framework for evolving multi-shaped detectors in negative selection[].Proceedings of IEEE Symposium Series on Computational Intelligence.2007
  • 8Wang W,Zhang P T,Tan Yet al.A hierarchical artificial immune model for virus detection[].International Conference on Computational Intelligence and Security.2009
  • 9Forrest S,Perelson AS,Allen L,et al.Self-Nonself Discrimination in a Computer[].Proceedings of the IEEE Symposium on Research in Security and Privacy.1994
  • 10Forrest S,Hofmeyr SA,Somayaji A,et al.A sense of self for unix processes[].Proceedings of the IEEE Symposium on Research in Security and Privacy.1996

共引文献3

同被引文献2

引证文献1

二级引证文献1

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