Adaptive and dynamic intrusion detection by means of idiotypic networks paradigm

In this paper we present a novel intrusion detection architecture based on idiotypic network theory (INIDS), that aims at dealing with large scale network attacks featuring variable properties, like denial of service (DoS). The proposed architecture performs dynamic and adaptive clustering of the network traffic for taking fast and effective counter-measures against such high-volume attacks. INIDS is evaluated on the MIT´99 dataset and outperforms previous approaches for DoS detection applied to this set.