Abstract

In this paper, Ant Colony Optimization (ACO) for intrusion detection were suggested. Two steps of the planned methods have taken place. PCA, as an ACO preprocessor, is used at the first stage to eliminate realistic vector calculations and reduce preparation time. To increase interface noise and increase the efficiency of ACO for a particular end objective. The second step is used to differentiate recognition using an ACO algorithm. ACO uses work adaptation in the hunting process. In the end, the device weights and ACO parameters are modified to the maximal device subset simultaneously. The tests were carried out using a data set of KDD 99, which were considered to be an agreed standard to test the quality of intrusion sensing to show that the proposed approach was sufficient. In fact, it is sensible to apply our hybridization approach correctly and effectively.