Intrusion Detection and Classification Using Ant Colony Optimization Algorithm

Studies of ant colonies have contributed in abundance to the set ofintelligent algorithms. The modeling of pheromone depositing by ants intheir search for the shortest paths to food sources resulted in thedevelopment of shortest path optimization algorithms. Ant colonyoptimization (ACO) algorithms have been successfully applied tocombinatorial optimization tasks especially to data mining classificationproblem.Internet and local networks have become everywhere. Soorganizations are increasingly employing various systems that monitorIT security breaches because intrusion events are growing day by day.Ant-based algorithms or ant colony optimization (ACO) algorithmscan be applied to the data mining field to extract rule-based classifiersand have been applied successfully to combinatorial optimizationproblems. More recently, researches applied ACO to data miningclassification problems, where they introduced a classification algorithmcalled Ant-Miner algorithm. The Ant-Miner algorithm is based on thebehavior of ants in searching of food. The aim of this paper is to use anAnt Colony-based classification system (Ant_Miner algorithm) to extracta set of rules for detection and classification, and it obtained a hopefulclassification accuracy.