The Dendritic Cell Algorithm for Intrusion Detection

TL;DR

The paper reviews the Dendritic Cell Algorithm (DCA), an Artificial Immune System approach, demonstrating its potential and improvements for effective online intrusion detection and anomaly identification.

Contribution

It introduces the DCA for intrusion detection, compares it with other AIS paradigms, and discusses recent enhancements for online anomaly detection.

Findings

Preliminary results show promising online detection capabilities.

Improvements include segmentation and automated data preprocessing.

DCA outperforms some existing methods in preliminary tests.

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.