The DCA:SOMe Comparison A comparative study between two biologically-inspired algorithms

The Dendritic Cell Algorithm (DCA) is an immune-inspired algorithm, developed for the purpose of anomaly detection. The algorithm performs multi-sensor data fusion and correlation which results in a `context aware' detection system. Previous applications of the DCA have included the detection of potentially malicious port scanning activity, where it has produced high rates of true positives and low rates of false positives. In this work we aim to compare the performance of the DCA and of a Self-Organizing Map (SOM) when applied to the detection of SYN port scans, through experimental analysis. A SOM is an ideal candidate for comparison as it shares similarities with the DCA in terms of the data fusion method employed. It is shown that the results of the two systems are comparable, and both produce false positives for the same processes. This shows that the DCA can produce anomaly detection results to the same standard as an established technique. Keywords Dendritic Cell Algorithm

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Julie Greensmith, Jan Feyereisl, Uwe Aickelin
• Keywords: Anomaly Detection, CORR 2010, Dendritic Cell Algorithm, Detection, Education