Welcome to the InfoSci Platform
Reference Hub8
The Dendritic Cell Algorithm for Intrusion Detection

The Dendritic Cell Algorithm for Intrusion Detection

Feng Gu, Julie Greensmith, Uwe Aickelin
Copyright: © 2012 |Pages: 19
ISBN13: 9781613500927|ISBN10: 1613500920|EISBN13: 9781613500934
DOI: 10.4018/978-1-61350-092-7.ch005
Cite Chapter Cite Chapter

MLA

Gu, Feng, et al. "The Dendritic Cell Algorithm for Intrusion Detection." Biologically Inspired Networking and Sensing: Algorithms and Architectures, edited by Pietro Lio and Dinesh Verma, IGI Global, 2012, pp. 84-102. https://doi.org/10.4018/978-1-61350-092-7.ch005

APA

Gu, F., Greensmith, J., & Aickelin, U. (2012). The Dendritic Cell Algorithm for Intrusion Detection. In P. Lio & D. Verma (Eds.), Biologically Inspired Networking and Sensing: Algorithms and Architectures (pp. 84-102). IGI Global. https://doi.org/10.4018/978-1-61350-092-7.ch005

Chicago

Gu, Feng, Julie Greensmith, and Uwe Aickelin. "The Dendritic Cell Algorithm for Intrusion Detection." In Biologically Inspired Networking and Sensing: Algorithms and Architectures, edited by Pietro Lio and Dinesh Verma, 84-102. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-61350-092-7.ch005

Export Reference

Mendeley
Favorite

Abstract

As one of the solutions to intrusion detection problems, Artificial Immune Systems (AIS) have shown their advantages. Unlike genetic algorithms, there is no one archetypal AIS, instead there are four major paradigms. Among them, the Dendritic Cell Algorithm (DCA) has produced promising results in various applications. The aim of this chapter is to demonstrate the potential for the DCA as a suitable candidate for intrusion detection problems. We review some of the commonly used AIS paradigms for intrusion detection problems and demonstrate the advantages of one particular algorithm, the DCA. In order to clearly describe the algorithm, the background to its development and a formal definition are given. In addition, improvements to the original DCA are presented and their implications are discussed, including previous work done on an online analysis component with segmentation and ongoing work on automated data pre-processing. Based on preliminary results, both improvements appear to be promising for online anomaly-based intrusion detection.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.