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Cited by 353

The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.

An artificial immune system architecture for computer security applications

Harmer, P.K., Williams, P.D., Gunsch, G.H., Lamont, G.B.·2002·IEEE Transactions on Evolutionary Computation

Abstract

With increased global interconnectivity and reliance on e-commerce, network services and Internet communication, computer security has become a necessity. Organizations must protect their systems from intrusion and computer virus attacks. Such protection must detect anomalous patterns by exploiting known signatures while monitoring normal computer programs and network usage for abnormalities. Current anti-virus and network intrusion detection (ID) solutions can become overwhelmed by the burden of capturing and classifying new viral strains and intrusion patterns. To overcome this problem, a self-adaptive distributed agent-based defense immune system based on biological strategies is developed within a hierarchical layered architecture. A prototype interactive system is designed, implemented in Java and tested. The results validate the use of a distributed-agent biological system approach toward the computer security problems of virus elimination and ID.

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Crossref · 10.1109/tevc.2002.1011540

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Metadata

Authors
Harmer, P. K., Williams, P. D., Gunsch, G. H., & Lamont, G. B.
Venue
IEEE Transactions on Evolutionary Computation
Published
2002-06
Volume
6
Issue
3
Pages
252-280
ISSN
  • 1089-778X (ISSN-L)
  • 1941-0026
OpenAlex
W2135560526
Cited by
353
References
22
FWCI
12.07
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: An artificial immune system architecture for computer security applications. https://app.cochat.ai/papers/10.1109/tevc.2002.1011540
The paper
Harmer, P. K., Williams, P. D., Gunsch, G. H., & Lamont, G. B. (2002). An artificial immune system architecture for computer security applications. IEEE Transactions on Evolutionary Computation. https://doi.org/10.1109/tevc.2002.1011540

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.