Ahlia University Manama Bahrain, EPITECH Paris France

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Abstract—Nowadays any intrusion detection system should include decision making feature. Each network administrator, in his everyday job, is overwhelmed with a big number of events and alerts. It is a challenge to be able to take correct decisions and to classify events according to their accuracy. That’s why we need to provide the administrator with the right tools in order to help him taking the correct decision. For this purpose, we suggest an Artificial Neural Networks (ANN) architecture for decision making within intrusion detection systems. Having in mind our IMA IDS solution [20] that presents a global agent architecture for enhanced intrusion network based solution, we are including ANN as a major decision algorithm using the learning and adaptive features of ANN. This inclusion aims to increase respectively efficiency, by reducing the fault positive, and detection capabilities by allowing detection with partial available information on the network status.

Index Terms—Artificial Neural Networks, Distributed Intrusion Detection System, Anomaly Detection, Signature based IDS.

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Cite:Nabil EL KADHI, Karim HADJAR, Nahla EL ZANT, "A Mobile Agents and Artificial Neural Networks for Intrusion Detection," Journal of Software vol. 7, no.1, pp. 156-160, 2012.