Unknown Network Attacks Research Articles

An Immune Intrusion Detection Model Based on One-Class Classification Technology

An intrusion detection model based on biological immune principle and one-class classification technology is proposed. The one-class classification technology named support vector domain description (SVDD) is applied to the proposed model. Simple multi-dimension feature vectors of network packets are mapped into high dimension feature space. The description models of the antibody and the self set are constructed. The evolution process of antibodies is described with math language. The theoretical analysis shows that the proposed model can detect network attack effectively, and unknown network attacks can be detected.

An Immune Intrusion Detection Model Based on One-Class Classification Technology

An intrusion detection model based on biological immune principle and one-class classification technology is proposed. The one-class classification technology named support vector domain description (SVDD) is applied to the proposed model. Simple multi-dimension feature vectors of network packets are mapped into high dimension feature space. The description models of the antibody and the self set are constructed. The evolution process of antibodies is described with math language. The theoretical analysis shows that the proposed model can detect network attack effectively, and unknown network attacks can be detected.

Detecting New Forms of Network Intrusion Using Genetic Programming

How to find and detect novel or unknown network attacks is one of the most important objectives in current intrusion detection systems. In this paper, a rule evolution approach based on Genetic Programming (GP) for detecting novel attacks on networks is presented and four genetic operators, namely reproduction, mutation, crossover, and dropping condition operators, are used to evolve new rules. New rules are used to detect novel or known network attacks. A training and testing dataset proposed by DARPA is used to evolve and evaluate these new rules. The proof of concept implementation shows that a rule generated by GP has a low false positive rate (FPR), a low false negative rate and a high rate of detecting unknown attacks. Moreover, the rule base composed of new rules has high detection rate with low FPR. An alternative to the DARPA evaluation approach is also investigated.