The Deterministic Dendritic Cell Algorithm

TL;DR

This paper introduces a deterministic version of the Dendritic Cell Algorithm, allowing for better analysis and control, tested on port scan data, with new parameters, effects of time windows, and a novel output metric.

Contribution

It presents a deterministic adaptation of the original stochastic Dendritic Cell Algorithm, enabling controlled experimentation and improved output assessment.

Findings

Deterministic version offers controllability and easier analysis.

Time windows and cell number variations influence algorithm performance.

A new, more sensitive metric for output assessment is proposed.

Abstract

The Dendritic Cell Algorithm is an immune-inspired algorithm orig- inally based on the function of natural dendritic cells. The original instantiation of the algorithm is a highly stochastic algorithm. While the performance of the algorithm is good when applied to large real-time datasets, it is difficult to anal- yse due to the number of random-based elements. In this paper a deterministic version of the algorithm is proposed, implemented and tested using a port scan dataset to provide a controllable system. This version consists of a controllable amount of parameters, which are experimented with in this paper. In addition the effects are examined of the use of time windows and variation on the number of cells, both which are shown to influence the algorithm. Finally a novel metric for the assessment of the algorithms output is introduced and proves to be a more sensitive metric than the metric used with the original Dendritic Cell Algorithm.