International Journal of Information Technology & Computer Science ( IJITCS )
Due to the rapid increase of internet users and computer networks these days, there is an increased need for effective security monitoring systems, such as Network Intrusion Detection Systems. Many researchers concentrate their efforts on this area using different type of approaches to build reliable detection system to protect computer networks from attacks. Flow-based intrusion detection systems are one of these approaches that rely on aggregated traffic metrics. Their main advantages are host independence and usability on high speed networks. In this paper, Neural Network anomaly intrusion detection system based on flow data is proposed for detecting attacks in the network traffic. The experimental results demonstrate that the designed models are promising in terms of accuracy and computational time, and low rates of false positive alarms .
: Intrusion Detection system, Neural Network, NetFlow, Multilayer Perceptron, Anomaly detection
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