Browsing by Author "Adeyemo, Victor Elijah"
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Item Heterogeneous Ensemble Models For Generic Classification(Computers and Applied Computer Science Faculty in "Tibiscus" University of Timişoara, Romania., 2017-05-10) Balogun, Abdullateef Oluwagbemiga; Balogun, Adedayo Miftaudeen; Sadiku, Peter Ogirima; Adeyemo, Victor ElijahThis paper presents the application of somedata mining techniques in the field of health care and computer network security. The selected classifiers wer eused individually and also, they were ensemble methods using four different combinations for the purpose of classification. Naïve Bayes, Radial Basis Function and Ripper algorithms were selected and the ensemble methods were majority voting, multi-scheme, stacking and Minimum Probability. The KDDCup’99 dataset was used as the benchmark for computer network security, while for the health care, breast cancer and diabetes dataset from the WEKA repository were used. All experiments and simulations were carried out, analyzed and evaluated using the WEKA tool. The Multi-scheme ensemble method gave the best accuracy result for the KDD dataset (99.81%) and the breast cancer dataset (73.08%) but its value of (75.65%) on breast cancer is the least of them all. Ripper algorithm gave the best result accuracy (99.76%) on KDD dataset amongst the base classifier but it was slightly behind in the breast cancer and diabetes dataset.Item Influence of Feature Selection On Multi-Layer Perceptron Classifier for Intrusion Detection System(Research Nexus Africa’s Networks in Conjunction with The African Institute of Development Informatics & Policy (AIDIP) Ghana & The International Centre for Information Technology & Development (ICITD), USA., 2016-12-15) Mabayoje, Modinat Abolore; Balogun, Abdullateef Oluwagbemiga; Ameen, Ahmed Oloduowo; Adeyemo, Victor ElijahThe usage of the most popular neural network – Multilayer perceptron, as gained ground for the purpose of detecting intrusion. A lot of researchers had used it judiciously but there exist problem of slow training time and data over-fitting. This paper reviews the various data mining techniques for applied in the area intrusion detection, categories of attacks, and techniques for feature selection. This paper proposes an architecture where information gain is used for feature selection and multilayer perceptron (MLP) for classification on KDD’99 dataset. Evaluation of the performance of the MLP classifier on the KDD’99 dataset and also on the reduced dataset was conducted.