Network Intrusion Detection by Using Cellular Neural Network with Tabu Search

This paper presents a novel cellular neural network (CNN) templates learning approach based on Tabu search (TS) for detecting network intrusions. The TS method was applied to CNN with symmetric templates and was verified by simulations. Simulation experiments on intrusion detection have shown that the TS-based template learning algorithm exhibits superior performance in computation time to find the optimal solution and in the solution quality in contrast to the algorithm of genetic algorithm (GA) and simulated annealing (SA).