Intrusion detection approach based on artificial idiotypic network

To overcome defects existing in methods based on neural networks, such as the periodical update on detectors and poor performance on unknown attacks, the memory, learning and dynamic regulating abilities of artificial idiotypic networks are used to implement intrusion detection approaches. A multi-mutation-pattern artificial idiotypic network is presented to be used as detectors. By utilizing the immune response principle, the detection algorithm is designed. New behavior features are learnt by detectors in real-time. The detection approach based on multi-mutation-pattern artificial idiotypic network is compared with the detection approach based on multilayer perceptrons through simulations. The results show that the average false positive rate is decreased by 17.43% and the average detection accuracy of unknown attacks is increased by 24.27%.