Detection of DDoS Attacks via an Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm


A Distributed Denial of Service Attack is a coordinated attack on the availability of services of a victim system, launched indirectly through many compromised computers. Intrusion detection systems (IDS) are network security tools that process local audit data or monitor network traffic to search for specific patterns or certain deviations from expected behavior. We use an Artificial Immune System (AIS) as a method of anomaly-based IDS because of the similarity between the IDS architecture and the Biological Immune Systems. We improved the jREMISA study; a Multiobjective Evolutionary Algorithm inspired AIS, in order to get better true and false positive rates while detecting DDoS attacks on the MIT DARPA LLDOS 1.0 dataset. We added the method of r-continuous evaluations, changed the Negative Selection and Clonal Selection structure, and redefined the objectives while keeping the general concepts the same. The 100% true positive rate and 0% false positive rate of our approach, under the given parameter settings and experimental conditions, shows that it is very successful as an anomaly-based IDS for DDoS attacks.