Dendritic Cells for Anomaly Detection

Greensmith, Julie; Twycross, Jamie; Aickelin, Uwe

Abstract

Artificial immune systems, more specifically the negative selection algorithm, have previously been applied to intrusion detection. The aim of this research is to develop
an intrusion detection system based on a novel concept in
immunology, the Danger Theory. Dendritic Cells (DCs) are
antigen presenting cells and key to the activation of the human immune system. DCs perform the vital role of combining
signals from the host tissue and correlate these signals with proteins known as antigens. In algorithmic terms, individual DCs perform multi-sensor data fusion based on time-windows. The whole population of DCs asynchronously correlates the fused signals with a secondary data stream. The behaviour of human DCs is abstracted to form the DC Algorithm (DCA), which is implemented using an immune inspired framework, libtissue. This system is used to detect context switching for a basic machine learning dataset and to detect outgoing portscans in real-time. Experimental results show a significant difference between an outgoing portscan and normal traffic.

Citation

Greensmith, J., Twycross, J., & Aickelin, U. Dendritic Cells for Anomaly Detection. Presented at Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2006)

• Conference Name: Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2006)
• Deposit Date: Oct 17, 2007
• Peer Reviewed: Peer Reviewed
• Public URL: https://nottingham-repository.worktribe.com/output/1018979