Abstract
Due to the convergence of new communication technologies to compatible platforms, the number of intrusions into computer systems is growing the attacks carried out by malicious users to exploit the vulnerabilities of these systems are more and more frequent; In this context, intrusion detection systems (IDS) have emerged as a group of methods that combats the unauthorized use of a network’s resources. Recent advances in information technology, especially in machine learning, have produced a wide variety of methods, which can be integrated into an IDS. This paper presents a technique of intrusion detection based on pre-treatment of data set and classification intrusions with a Self Organizing Map (SOM) Artificial Neural Network method optimized with Genetic algorithm (GA) to develop a model for intrusion detection system. The simulation results show a significant improvement in detection rate. The performance of the proposed method of intrusion detection was evaluated on all UNSW-nb15 and KDD99 data sets.
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Moukhafi, M., Yassini, K.E., Bri, S., Oufaska, K. (2019). Artificial Neural Network Optimized by Genetic Algorithm for Intrusion Detection System. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_35
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DOI: https://doi.org/10.1007/978-3-030-11928-7_35
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