1,756 research outputs found
Dendritic Cells for Anomaly Detection
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 signals from the host tissue and
correlate these signals with proteins know 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.Comment: 8 pages, 10 tables, 4 figures, IEEE Congress on Evolutionary
Computation (CEC2006), Vancouver, Canad
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