Network Intrusion Detection Systems Using Neural Networks

With the growth of network activities and data sharing, there is also increased risk of threats and malicious attacks. Intrusion detection refers to the act of successfully identifying and thwarting malicious attacks. Traditionally, the help of network security experts is sought owing to their familiarity with the network technologies and broad knowledge. Recently, data mining techniques have been increasingly adopted to perform network intrusion detection. This paper presents the comparison between multi-layer perceptron and radial basis function networks for designing network intrusion detection system. Multi-layer perceptron proved to be more effective than radial basis function when applied on the benchmark NSL_KDD dataset.