An Immune Inspired Approach to Anomaly Detection

TL;DR

This paper introduces a second-generation artificial immune system for process anomaly detection, emphasizing diverse cell types and communication for improved security performance.

Contribution

It advances previous immune-inspired security methods by incorporating multiple cell types and their interactions, enhancing anomaly detection capabilities.

Findings

Communication between cell types improves detection accuracy

Second-generation systems outperform earlier models

Capable of detecting anomalies beyond generic policies

Abstract

The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic a view of the immune system. We present here a second generation artificial immune system for process anomaly detection. It improves on earlier systems by having different artificial cell types that process information. Following detailed information about how to build such second generation systems, we find that communication between cells types is key to performance. Through realistic testing and validation we show that second generation artificial immune systems are capable of anomaly detection beyond generic system policies. The paper concludes with a discussion and outline of the next steps in this exciting area of computer security.