Papers by Omonigho Ekeruvwe
Ilorin Journal of Computer Science and Information Technology, 2022
It is crucial to avoid intrusion in networks; hence, a developing and intrusion detection system ... more It is crucial to avoid intrusion in networks; hence, a developing and intrusion detection system that used a strong mechanism for detecting intrusions is important. Several studies have been conducted in the domain of intrusion detections. However, some of them suffer from high false alarms, in terms of the use of a raw dataset with redundancy. Objective: This paper, therefore, proposes a multi-level dimensionality reduction framework that is based on meta-heuristic optimization and Principal Component Analysis (PCA). Method: In this research, PCA was applied for feature extraction. Genetic Algorithm and Particle Swarm Optimization, that is GA-PSO, algorithms were utilized for feature selection to extract the most discriminative features to develop intrusion detection model. In the classification phase, both Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms were used to develop intrusion detection, using kddcup.data_10_percent dataset. Result: Experimental results reveal that the proposed framework brought about an accuracy of 99.7% and ROC of 99.9%, while the time required building model is 0.23 seconds. Conclusion: To a very high extent, incidences of high false alarm are allayed through the GA-PSO induced feature selection method.
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Papers by Omonigho Ekeruvwe