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Immune based computer virus detection approaches

TAN Ying ZHANG Pengtao

智能系统学报2013,Vol.8Issue(1):80-94,15.
智能系统学报2013,Vol.8Issue(1):80-94,15.DOI:10.3969/j.issn.1673-4785.201209059

Immune based computer virus detection approaches

Immune based computer virus detection approaches

TAN Ying 1ZHANG Pengtao1

作者信息

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摘要

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.

关键词

computer virus detection/artificial immune system/immune algorithms/hierarchical model/negative selection algorithm with penalty factor

Key words

computer virus detection/artificial immune system/immune algorithms/hierarchical model/negative selection algorithm with penalty factor

分类

信息技术与安全科学

引用本文复制引用

TAN Ying,ZHANG Pengtao..Immune based computer virus detection approaches[J].智能系统学报,2013,8(1):80-94,15.

基金项目

National Natural Science Foundation of China (No.61170057,60875080). (No.61170057,60875080)

智能系统学报

OA北大核心CSCDCSTPCD

1673-4785

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