Ant Colony Optimization with Classification Algorithms used for Intrusion Detection

IDS which are increasingly a key part of system defense are used to identify abnormal activities in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining and machine learning techniques have been used in the literature. During recent years, number of attacks on networks has dramatically increased and consequently interest in network intrusion detection has increased among the researchers. In this paper we have used the terms detection rates and false alarm rates to compare the results of Naïve Bayes algorithm and Support Vector Machine algorithm to find out the results for intrusion detections and by using Naïve Bayes method with Ant Colony Optimization technique try to improve the rates for better detection. The proposed algorithm is used for comparative study we have done in this paper on the basis of which we measure the performance and usefulness of particular methods in detecting specific class of attacks. Experimental results performed using the KDD99 benchmark network intrusion detection dataset indicate that it can significantly reduce the number and percentage of false positives and scale up the balance detection rates for different types of network intrusions.