Artificial Neural Networks Architecture For Intrusion Detection Systems and Classification of Attacks

The ubiquity of the Internet poses serious concerns on the security of computer infrastructures and the integrity of sensitive data. Intrusion Detection Systems (IDS) aim at protecting networks and computers from malicious networkbased or host-based attacks. The underlying assumption of intrusion detection is an attack will noticeably affect system performance or behavior. Neural networks method is a promising technique, which has been used in many classification problems. The present study is aimed to solve a multi-class problem of intrusion detection using MLP in which not only the attack records are distinguished from normal ones, but also the attack type is identified. The results showed that the designed system is capable of classifying records with 93.43% accuracy with two hidden layers of neuron.