On convergence of Evolutionary Negative Selection Algorithms for anomaly detection

Evolutionary negative selection algorithms (ENSAs) are proposed by combining negative selection model and evolutionary operators. In this paper, the convergence of ENSAs with two different mutation operators is analyzed. The first mutation operator is that only one bit of a detector is selected and flipped with a high probability. The second mutation operator is that every bit of a detector has a positive probability to be flipped. The analysis results show that the ENSAs with different mutation operators have different convergent properties. Especially, the shape of the self set will affect the convergence of ENSAs with the first mutation operator.