Applying Local Search and Genetic Evolution in Concept Learning Systems to Detect Intrusion in Computer Networks

The detection of intrusions over computer networks (i.e., network access by non-authorized users) can be cast to the task of detecting anomalous patterns of network traffic. In this case, models of normal traffic have to be determined and compared against the current network traffic. We compare models of network traffic acquired by a system based on a distributed genetic algorithm with the ones acquired by a system based on greedy heuristics. Also we show that representation change of the network data can result in a significant increase in the classification performances of the traffic models. Network data made available from the Information Exploration Shootout project has been chosen as experimental testbed.