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Advanced probabilistic approach for network intrusion forecasting and detection

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SKU: PROJ1475 Categories: 2013 Projects, 2014 Projects, Final Year Projects, Networking Projects, Online Java Projects Tags: academic projects, Android project 2013-2014, Android project Abstract, Android project list, btech projects, dotnet project 2013-2014, dotnet project Abstract, dotnet project list, elysium technologies abstract, elysium technologies chennai, elysium technologies coimbatore, elysium technologies company, elysium technologies courses, elysium technologies erode, elysium technologies inpant traning, elysium technologies internship, elysium technologies jobs, elysium technologies madurai, elysium technologies mou, elysium technologies pondychery, elysium technologies projectlist, elysium technologies projects, elysium technologies ramnad, elysium technologies salem, elysium technologies software, elysium technologies tirunelveli, elysium technologies trichy, Final Year Projects, java project list, java projects 2013-2014, java projects Abstract, madurai software company, matlab project 2013-2014, matlab project Abstract, matlab project list, mtech projects, phd research work, Php project 2013-2014, Php project Abstract, Php project list, Power Electronic project 2013-2014, Power Electronic project Abstract, Power Electronic project list, project center Bangalore, project center chennai, project center coimbatore, project center Erode, project center Hyderabad, project center Kollam, project center madurai, project center Pandicherry, project center ramnad, project center Salem, project center Tiruneveli, project center trichy, research center, Students Projects, Vlsi project 2013-2014, Vlsi project Abstract, Vlsi project list
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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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