A Dynamic Immunity-Based Model for Computer Virus Detection

Li, Tao; Qin, Renchao

doi:10.1109/isip.2008.129

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…The experiments are carried on WildList computer virus set [8,9]. We select 500 computer viruses from it, including Tiny, Vienna, Jerusalem and so on.…”

Section: Resultsmentioning

confidence: 99%

A Novel Computer Virus Detection Method from Ideas of Immunology

Zeng

2009

2009 International Conference on Multimedia Information Networking and Security

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In this paper, inspired by the change of antibody concentration in biological immune system, we present a Novel Computer Virus Detection Method from Ideas of Immunology (NCVDMII). In NCVDMII, the concepts and formal definitions of antibody, antigen, nonself and detector are given. Then, NCVDMII get the specific antibody from the simulation of primary immune response and calculate the antibody number by using the secondary immune response. Finally, building upon the above processes, a probabilistic calculation method for computer virus alarm production, which is based on the correlation between the antibody concentration and the computer virus infection intensity, is proposed. Compared with Negative Selection Algorithm (NSA), Automatic Computer Immune System (ACIS) and Immunity-based System (IBS), the false alarm rate of NCVDMII is reduced by 17.5%, 5.0% and 2.8%, respectively.

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“…The experiments are carried on WildList computer virus set [8,9]. We select 500 computer viruses from it, including Tiny, Vienna, Jerusalem and so on.…”

Section: Resultsmentioning

confidence: 99%

A Novel Computer Virus Detection Method from Ideas of Immunology

Zeng

2009

2009 International Conference on Multimedia Information Networking and Security

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“…By computer system we mean associated network, hardware, software and operating system. The more viruses especially new viruses an antivirus detect, higher the ability it has [9] [11].…”

Section: Definitions Principles and Antivirus Performance Metricsmentioning

confidence: 99%

“…After that it generates specific antibodies from known virus vaccine gene, and non-specific antibodies created based on vaccine gene using crossover, mutation and other possible operations. If an unknown virus is identified by non-specific antibodies, its vaccine gene will be added in vaccine gene library [11]. Another method of vaccination is being proposed by Wang et al in [26].…”

Section: Immunity Based Modelmentioning

confidence: 99%

Selection of Next Generation Anti-Virus against Virus Attacks in Networks Using AHP

Paul¹,

Mishra²

2012

IJCNIS

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Defending against virus attacks in network is a vital part of network security. With the rapid evolution of viruses, its defense mechanism has also been evolved over the years. But given the diversity and complexity of virus propagation and its attack behavior, no defense mechanism is equipped fully to protect the network from such attacks. Several antiviruses are available in the market. But none can give full proof solution to malicious attacks in communication networks. In this paper we present a mechanism to measure and compare the relative ability of antivirus against various kinds of viruses. We construct a hierarchical structure for different virus defense mechanism. Using Analytical Hierarchy Process (AHP) we construct a pair wise comparison matrix and find the value of corresponding Eigen vectors; we then apply the theory of AHP to calculate weight of each defense index. We validated our technique with an example. Our method can provide a strong reference to design an optimal network security solution.

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“…The polymorphism of the biological viruses means chaining the structure of their protein, which corresponding to the change of signatures of the computer virus [17]. The algorithms used by the human immune system and those that participate in detecting and eliminating the polymorphic viruses can guide the research to devise a biological model for polymorphic detection.…”

Section: A Biological Form Of Computer Polymorphic Virusesmentioning

confidence: 99%

Devising a Biological Model to Detect Polymorphic Computer Viruses Artificial Immune System (AIM): Review

Abdulla

Zakaria

2009

2009 International Conference on Computer Technology and Development

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Currently, in most virus detection techniques the signature recognition process of unknown viruses are challenging task. The process has become more difficulties after the complexities of the viruses' structure have developed, as in the Polymorphic computer viruses. This paper reviews and then explains some used techniques to detect the known polymorphic viruses. Besides, the paper contains the review of some biological models for virus detection. Finally, a new biological model for detecting polymorphic viruses is suggested

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A Dynamic Immunity-Based Model for Computer Virus Detection

Cited by 8 publications

References 14 publications

A Novel Computer Virus Detection Method from Ideas of Immunology

A Novel Computer Virus Detection Method from Ideas of Immunology

Selection of Next Generation Anti-Virus against Virus Attacks in Networks Using AHP

Devising a Biological Model to Detect Polymorphic Computer Viruses Artificial Immune System (AIM): Review

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