Further Exploration of the Dendritic Cell Algorithm: Antigen Multiplier and Time Windows

TL;DR

This paper evaluates the Dendritic Cell Algorithm (DCA) on the KDD 99 dataset, exploring enhancements like antigen multiplier and time windows, and compares its performance with baseline algorithms such as NSA and C4.5.

Contribution

It demonstrates the applicability of DCA to KDD 99 data and assesses the impact of antigen multiplier and time windows on its performance.

Findings

DCA is applicable to KDD 99 dataset.

Antigen multiplier and time windows have similar effects on DCA.

NSA with constant-sized detectors is not suitable for this dataset.

Abstract

As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performances in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DXA, including the antigen multiplier and moving time windows are reported. The real-valued Negative Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision tree algorithm are used, to conduct a baseline comparison. The results suggest that the DCA is applicable to KDD 99 data set, and the antigen multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with constant-sized detectors is not applicable to the data set, and the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.