An intrusion detection system (IDS) is a device or software application that monitors network or system activities for malicious activities or policy violations and produces reports to a management station. Majority of research is going on neural network and machine learning technique for detecting intrusions. In this paper, we present a layered framework integrated with neural network to build an effective intrusion detection system. Here we make use of K-means clustering algorithm along with the back propagation neural networks algorithm. The proposed system is trained to detect three types of attacks UDP flood, TCP flood and Ping flood. The results show the system has high attack detection accuracy.
IDS, neural network, layered framework, K-means Algorithm
