Analysis of Different Architectures of Neural Networks for Application in Intrusion Detection Systems
Usually, Intrusion Detection Systems (IDS) work using two methods of identification of attacks: by signatures that are specific defined elements of the network traffic possible to identification and by anomalies being some deviations form of the network behavior assumed as normal. In the both cases one must pre-define the form of the signature (in the first case) and the network's normal behavior (in the second one). This paper proposes application of Neural Networks (NN) as a tool for application in IDS. Such a method makes possible utilization of the NN learning property to discover new attacks, so (after the training phase) one need not deliver attacks' definitions to the IDS.