IEEE Transactions on Software Engineering - Special issue on computer security and privacy
C4.5: programs for machine learning
C4.5: programs for machine learning
Exploring the Power of Genetic Search in Learning Symbolic Classifiers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining in a data-flow environment: experience in network intrusion detection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning Program Behavior Profiles for Intrusion Detection
Proceedings of the Workshop on Intrusion Detection and Network Monitoring
Search-intensive concept induction
Evolutionary Computation
Similarity-Based Classification in Relational Databases
Fundamenta Informaticae
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The detection of intrusions over computer networks can be cast to the task of detecting anomalous patterns of network traffic. In this case, patterns 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. In this paper we compare models of data traffic acquired by a system based on a distributed genetic algorithm with the ones acquired by a systembased on greedy heuristics. Also we discuss representation change of the network data and its impact over the performances of the traffic models.