Browsing by Author "Adeyemo, Victor Elijah"
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Item A Network Intrusion Detection System: Enhanced Classification via Clustering Model(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, 2015-12-10) Balogun, Abdullateef Oluwagbemiga; Balogun, Adedayo Miftaudeen; Adeyemo, Victor Elijah; Sadiku, Peter OgirimaThe aim of developing an IDS is to build a system that oversee the general protection of a network from attacks both from withinand without, and doing so accurately. Optimization of IDS has also being receiving attention from the research community due toits large volumes of security audit data. In developing an IDS, most dataset used have high dimension in which only few attributes are needed for building an IDS – feature selection is used to solve this little problem. In this paper, we present and analyze the performance of some machine learning algorithm which performs classification via clustering using the KDDcup’99 dataset. Using the WEKA tool, simulations were ran and results was deduced after applying the proposed models to the dataset containing all the type of attacksItem Parameter tuning in KNN for software defect prediction: an empirical analysis(Department of Computer Engineering, Universitas Diponegoro, Indonesia., 2019-10-31) Mabayoje, Modinat Abolore; Balogun, Abdullateef Oluwagbemiga; Jibril, Hajarah Afor; Atoyebi, Jelili Olaniyi; Mojeed, Hammed Adeleye; Adeyemo, Victor ElijahSoftware Defect Prediction (SDP) provides insights that can help software teams to allocate their limited resources in developing software systems. It predicts likely defective modules and helps avoid pitfalls that are associated with such modules. However, these insights may be inaccurate and unreliable if parameters of SDP models are not taken into consideration. In this study, the effect of parameter tuning on the k nearest neighbor (k-NN) in SDP was investigated. More specifically, the impact of varying and selecting optimal k value, the influence of distance weighting and the impact of distance functions on k-NN. An experiment was designed to investigate this problem in SDP over 6 software defect datasets. The experimental results revealed that k value should be greater than 1 (default) as the average RMSE values of k-NN when k>1(0.2727) is less than when k=1(default) (0.3296). In addition, the predictive performance of k-NN with distance weighing improved by 8.82% and 1.7% based on AUC and accuracy respectively. In terms of the distance function, kNN models based on Dilca distance function performed better than the Euclidean distance function (default distance function). Hence, we conclude that parameter tuning has a positive effect on the predictive performance of k-NN in SDP.Item Performance Analysis of Selected Clustering Techniques for Software Defects Prediction(IEEE NIgeria Chapter, 2019-06-01) Balogun, Abdullateef; Oladele, Rufus; Mojeed, Hammed; Amin-Balogun, Barakat; Adeyemo, Victor Elijah; Aro, Taye OlalereClassification algorithms that help to predict software defects play a major role in the software engineering process. This study investigated the application and performance of clustering techniques in software defect prediction (SDP). Seven clustering techniques; Farthest First Clusterer, K-Means, X-Means, Sequential information Bottleneck, Hierarchical Clusterer, Make-Density Clusterer, and Expectation Maximization were used for the classification of 8 software defect datasets from NASA repository. Experimental results revealed that the use of clustering technique as a classification process is well established as it gave a good predictive performance. Based on average accuracy across the 8 datasets, Farthest First had the best performance of 86.16%, Hierarchical clustering had 85.50% while KMeans Clustering techniques had 72.33% respectively. Expectation Maximization (EM) (33.52%) and X-Means (48.84%) gave rather poor results and Sequential Information bottleneck (SIB) (63%) and Density-based clustering techniques (71.08%) had average performances. In addition, further comparison of classification via clustering techniques with selected standard classification techniques; k-Nearest Neighbor (kNN), Naïve Bayes (NB), and Decision Tree (DT) showed that some classification via clustering techniques (Farthest First and Hierarchical Clustering Techniques) performed considerably well and outperforms some standard classification algorithms. With this, classification via clustering techniques can be considered as an alternative approach to standard classification methods in SDP. It produced a good and competitive predictive performance in SDP with an advantage of not necessarily training a predictive model and using annotated datasets while developing the predictive model. Consequently, SDP models developed using classification via clustering techniques models can be transferred from one project to another as no training of the model is involved. This will help reduce and manage the available resources during the software development process.