Intrusion Detection with Artificial Neural Networks

Intrusion detection system is a detection mechanism that detects unauthorized, malicious presents in the computer systems. The purpose of this book is to design, implement and evaluate an anomaly based network intrusion detection system. The System learns about the normal users' behavior and finds the anomalies by matching with this normal behavior. A special type of neural network called backpropagation neural network is used for learning normal users' behavior. The network traffic that only contains information of normal users is presented with the neural network for learning about the normal users' behavior. The system performance has been tested by using a simulated computer network. The neural network is trained with huge,not so huge and small amount of data. The detection capability of the system has been tested with huge and small amount of data. It is seen from the performance analysis that the system performs well when trained with small amount of data. An overall detection rate of 98% has been achieved for both known and unknown attacks. Moreover, the system can detect 100% normal user.

Autorentext

Moazzam Hossain received his M. Sc. in Computer Systems Engineering from The Technical University of Denmark. He is currently working as an assistant professor of Computer Science & Engineering at International Islamic University Chittagong, Bangladesh. His research interest includes Computer and Web Security, Artificial Intelligence etc.