Advanced probabilistic approach for network intrusion forecasting and detection
Abstract— Advanced probabilistic approach for network intrusion forecasting and detection. This study aims to propose a probabilistic approach for detecting network intrusions using Bayesian Networks (BNs). Three variations of BN, namely, Naïve Bayesian Network (NBC), Learned BN, and hand-crafted BN, were evaluated and from which, an optimal BN was obtained. A standard dataset containing 494020 records, a category for normal network traffics, and four major attack categories (Denial of Service, Probing, Remote to Local, User to Root and Normal), < Final Year Projects > were used in this study. The dataset went through an 80-20 split to serve the training and testing phases. 80% of the dataset were treated with a feature selection algorithm to obtain a set of features, from which the three BNs were constructed. During the evaluation phase, the remaining 20% of the dataset were used to obtain the classification accuracies of the BNs. The results show that the hand-crafted BN, in general, has outperformed NBC and Learned BN.
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