Decision tree based learning and genetic based learning to detect network intrusions
Filippo Neri · SMO'05 Proceedings of the 5th WSEAS international conference on Simulation, modelling and optimization · 2005
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.