An Agent Based Classification Model

TL;DR

This paper explores implementing an immune-inspired anomaly detection model using agent-based simulation to enhance the development of adaptive cybersecurity systems.

Contribution

It demonstrates the feasibility of re-implementing the Dendritic Cell Algorithm within an agent-based simulation environment, enabling more complex immune-inspired models.

Findings

Successful implementation of DCA in AnyLogic environment

Potential for developing more adaptive AIS models

Enhanced simulation capabilities for immune-inspired systems

Abstract

The major function of this model is to access the UCI Wisconsin Breast Can- cer data-set[1] and classify the data items into two categories, which are normal and anomalous. This kind of classifi cation can be referred as anomaly detection, which discriminates anomalous behaviour from normal behaviour in computer systems. One popular solution for anomaly detection is Artifi cial Immune Sys- tems (AIS). AIS are adaptive systems inspired by theoretical immunology and observed immune functions, principles and models which are applied to prob- lem solving. The Dendritic Cell Algorithm (DCA)[2] is an AIS algorithm that is developed specifi cally for anomaly detection. It has been successfully applied to intrusion detection in computer security. It is believed that agent-based mod- elling is an ideal approach for implementing AIS, as intelligent agents could be the perfect representations of immune entities in AIS. This model evaluates the feasibility of re-implementing the DCA in an agent-based simulation environ- ment called AnyLogic, where the immune entities in the DCA are represented by intelligent agents. If this model can be successfully implemented, it makes it possible to implement more complicated and adaptive AIS models in the agent-based simulation environment.