[1]TAN Ying,ZHANG Pengtao.Immune based computer virus detection approaches[J].智能系统学报,2013,8(01):80-94.[doi:10.3969/j.issn.1673-4785.201209059]
 TAN Ying,ZHANG Pengtao.Immune based computer virus detection approaches[J].CAAI Transactions on Intelligent Systems,2013,8(01):80-94.[doi:10.3969/j.issn.1673-4785.201209059]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第8卷
期数:
2013年01期
页码:
80-94
栏目:
出版日期:
2013-03-25

文章信息/Info

Title:
Immune based computer virus detection approaches
文章编号:
1673-4785(2013)01-0080-15
作者:
TAN Ying12 ZHANG Pengtao12
Author(s):
TAN Ying12 ZHANG Pengtao12
1. Department of Machine Intelligence, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China;
 2. Key Laboratory of Machine Perception, Ministry of Education, Peking University, Beijing 100871, China
Keywords:
computer virus detection artificial immune system immune algorithms hierarchical model negative selection algorithm with penalty factor
分类号:
TP309.5
DOI:
10.3969/j.issn.1673-4785.201209059
文献标志码:
A
Abstract:
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.

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备注/Memo

备注/Memo:
Received Date: 2012-09-27.
Network Publishing Date: 2013-02-05.
Foundation Item: National Natural Science Foundation of China(No. 61170057, 60875080).
Corresponding Author: TAN Ying.
E-mail: ytan@pku.edu.cn.
About the authors:
TAN Ying(M′98, SM′02), male, born in 1964. He is a full professor, advisor for Ph.D. candidates at the Key Laboratory of Machine Perception (Ministry of Education), Peking University, and Department of Machine Intelligence, EECS, Peking University. His current research interests include computational intelligence, artificial immune system, swarm intelligence and data mining, signal and information processing, pattern recognition, and their applications.
ZHANG Pengtao, male, born in 1986. His research interests include artificial immune system, intelligent information processing algorithm, computer information security, pattern recognition, machine learning and data mining.
更新日期/Last Update: 2013-04-12