Vulnerability analysis of immunity-based intrusion detection systems using genetic and evolutionary hackers

Artificial immune systems (AISs) are biologically inspired problem solvers that have been used successfully as intrusion detection systems (IDSs). In this paper, we compare a genetic hacker with 12 evolutionary hackers based on particle swarm optimization (PSO) that have been effectively used as vulnerability analyzers (red teams) for AIS-based IDSs. Our results show that the PSO-based red teams that use Clerc's constriction coefficient outperform those that do not. Our results also show that the three types of red teams (genetic, basic PSO, and PSO with the constriction coefficient) have distinct search behaviors that are complimentary.

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