Using Ant Colony Algorithm and Pairwise Learning to Classify Attack in Intrusion Detection Systems

Intrusion detection systems for security in computer networks have been proposed to be crossed if the attacker from other security equipment, able to detect it and prevent it from advancing. One of the challenges of these systems, it is high dimensional data. In this study was to reduce the dimensions of a simple genetic algorithm with the length of the string variable we use. Then, according to selected characteristics, a meta-heuristic model for data classification, using ant colony algorithm offer. Classification model proposed by trying to divide the data into two samples is Hnjydh and Nahnjydh. The proposed method for evaluating the performance of database intrusion detection NSL-KDD than other data from the records of more realistic approach is used. The results of the experiments, the proposed method has better performance compared with other existing methods show.