The Dendritic Cell Algorithm for Intrusion Detection

Feng Gu, Julie Greensmith, U. Aickelin
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引用次数: 33

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 preprocessing. Based on preliminary results, both improvements appear to be promising for online anomaly-based intrusion detection.
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树突状细胞入侵检测算法
人工免疫系统(AIS)作为入侵检测的解决方案之一,已显示出其优势。与遗传算法不同,人工智能没有单一的原型,而是有四种主要的范式。其中,树突状细胞算法(DCA)在各种应用中都取得了可喜的成果。本章的目的是演示DCA的潜力作为入侵检测的合适的人选问题。我们回顾了一些用于入侵检测问题的常用AIS范例,并展示了一种特定算法DCA的优势。为了更清晰地描述该算法,给出了该算法的发展背景和形式化定义。此外,还介绍了对原始DCA的改进,并讨论了它们的含义,包括以前在带有分段的在线分析组件上所做的工作和正在进行的自动数据预处理工作。基于初步结果,这两种改进对于基于异常的在线入侵检测似乎都很有希望。
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