ABSTRACT
The rapid development and expansion of World Wide Web and local network systems have changed the computing world in the last decade. However, this outstanding achievement has a drawback. The highly connected computing world has also equipped the intruders and hackers with new facilities for their destructive purposes. The costs of temporary or permanent damages caused by unauthorized access of the intruders to computer systems have urged different organizations to increasingly implement various systems to monitor data flow in their networks. These systems are generally referred to as Intrusion Detection Systems (IDSs). There are two main approaches to the design of IDSs. In a misuse detection based IDS, intrusions are detected by looking for activities that correspond to known signatures of intrusions or vulnerabilities. On the other hand, anomaly detection based IDS detect intrusions by searching for abnormal network traffic.
In the present study, an off-line intrusion detection system is implemented using Multi-Layer Perceptron (MLP) artificial neural network. While in many previous studies the implemented system is a neural network with the capability of detecting normal or attack connections, in the present study a more general problem is considered in which the attack type is also detected. Fuzzy C-mean clustering is used to classify the input into different classes of clusters.
