Comparing local search with respect to genetic evolution to detect intrusions 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. Data mining systems based on genetic algorithms can contribute powerful search techniques for the acquisition of patterns of the network traffic from the large amount of data made available by audit tools. 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 provide an empirical proof 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 and the 1998 DARPA Intrusion Detection Evaluation have been chosen as experimental testbed.