A Neural Network Based Anomaly Intrusion Detection System

Security system is the immune system for computers which is similar to the immune system in the human body. This includes all operations required to protect computer and systems from intruders. The aim of this work is to develop an anomaly-based intrusion detection system (IDS) that can promptly detect and classify various attacks. Anomaly-based IDSs need to be able to learn the dynamically changing behavior of users or systems. In this paper, we are experimenting with packet behavior as parameters in anomaly intrusion detection. There are several methods to assist IDSs to learn system's behavior. The proposed IDS uses a back propagation artificial neural network (ANN) to learn system's behavior. We have used the KDD'99 data set in our experiments and the obtained results satisfy the work objective.

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