As the use of Internet increases, cyber attacks and their severity also increase. Since it is not possible to compromise on security, intrusion detection systems (IDSs) become critical component in a secure organization. IDSs detect an attack only after it has occurred. When use in a high-traffic network, IDSs produce a large number of alerts. The false-positive (FP) rate increases with this. In this paper, we propose a framework for predicting future attacks by combining two machinelearning methods: genetic algorithm (GA) and hidden Markov model (HMM). It has two major components in which the first component makes use of GA to derive efficient intrusion detection rules and thereafter a precise detection of attacks. The second component uses HMM to predict the next attack class of the attacker. So combining these together is a good idea and gives a good intrusion prediction capability with reduced FP rate.
CITATION STYLE
Divya, T., & Muniasamy, K. (2015). Real-time intrusion prediction using hidden Markov model with genetic algorithm. In Advances in Intelligent Systems and Computing (Vol. 324, pp. 731–736). Springer Verlag. https://doi.org/10.1007/978-81-322-2126-5_78
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