ACM Transactions on Information and System Security (TISSEC)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Cluster validation techniques for genome expression data
Signal Processing - Special issue: Genomic signal processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Nonparametric genetic clustering: comparison of validity indices
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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This paper represents the first step in an on-going work for designing an unsupervised method based on genetic algorithm for intrusion detection. Its main role in a broader system is to notify of an unusual traffic and in that way provide the possibility of detecting unknown attacks. Most of the machine-learning techniques deployed for intrusion detection are supervised as these techniques are generally more accurate, but this implies the need of labeling the data for training and testing which is time-consuming and error-prone. Hence, our goal is to devise an anomaly detector which would be unsupervised, but at the same time robust and accurate. Genetic algorithms are robust and able to avoid getting stuck in local optima, unlike the rest of clustering techniques. The model is verified on KDD99 benchmark dataset, generating a solution competitive with the solutions of the state-of-the-art which demonstrates high possibilities of the proposed method.