A Three-Tier Model for Intrusions Classification on a Computer Network
Abstract
Activities of cyber attackers are on the rampage; this is because there is an increase in the usage of computer related applications. Attackers have caused reputational and economic damages to network administrators, companies and industries based on the information they have stolen. To curb all these activities, a formidable Intrusion Detection System (IDS) is needed to guide against all the numerous cyber-attacks. The research work solely aimed at reducing the accessibility of cyber threats by bringing its operations to as minimal as possible because of the adverse effects they have had in the past. This research proposed a three-tier IDS which classifies the various attacks into their various groups. The proposed model consists of Bayes Network (BN), Support Vector Machine (SVM), and Artificial Neural Network (ANN). NLS KDD 99 dataset was used for simulating the proposed three-tier IDS in the WEKA environment. The effectiveness and efficiency of the proposed model was based on recall, precision, and accuracy. The proposed three-tier model gave the following results: recall: 0.993; precision: 0.979; accuracy: 0.986.
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