Intrusion Detection and Classification Using Ant Colony Optimization Algorithm

Abstract

Studies of ant colonies have contributed in abundance to the set of intelligent algorithms. The modeling of pheromone depositing by ants in their search for the shortest paths to food sources resulted in the development of shortest path optimization algorithms. Ant colony optimization (ACO) algorithms have been successfully applied to combinatorial optimization tasks especially to data mining classification problem. Internet and local networks have become everywhere. So organizations are increasingly employing various systems that monitor IT security breaches because intrusion events are growing day by day. Ant-based algorithms or ant colony optimization (ACO) algorithms can be applied to the data mining field to extract rule-based classifiers and have been applied successfully to combinatorial optimization problems. More recently, researches applied ACO to data mining classification problems, where they introduced a classification algorithm called Ant-Miner algorithm. The Ant-Miner algorithm is based on the behavior of ants in searching of food. The aim of this paper is to use an Ant Colony-based classification system (Ant_Miner algorithm) to extract a set of rules for detection and classification, and it obtained a hopeful classification accuracy.