CLASSIFICATION OF CYBERSECURITY INCIDENTS IN NIGERIA USING MACHINE LEARNING METHODS

Onyinye Onyekpeze, Olumide Owolabi, Bisalla Hashim Ibrahim

Abstract


Cybercrime has become more likely as a result of technological advancements and increased use of the internet and computer systems. As a result, there is an urgent need to develop effective methods of dealing with these cyber threats or incidents to identify and combat the associated cybercrimes in Nigerian cyberspace adequately. It is therefore desirable to build models that will enable the Nigeria Computer Emergency Response Team (ngCERT) and law enforcement agencies to gain valuable knowledge of insights from the available data to detect, identify and efficiently classify the most prevalent cyber incidents within Nigeria cyberspace, and predict future threats. This study applied machine learning methods to study and understand cybercrime incidents or threats recorded by ngCERT to build models that will characterize cybercrime incidents in Nigeria and classify cybersecurity incidents by mode of attacks and identify the most prevalent incidents within Nigerian cyberspace. Seven different machine learning methods were used to build the classification and prediction models. The Logistic Regression (LR), Naïve Bayes (NB), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Tree (CART) and Random Forest (RF) Algorithms were used to discover the relationship between the relevant attributes of the datasets then classify the threats into several categories. The RF, CART, and KNN models were shown to be the most effective in classifying our data with accuracy score of 99%  each while others has accuracy scores of 98% for SVM, 89% for NB, 88% for LR, and 88% for LDA. Therefore, the result of our classification will help organizations in Nigeria to be able to understand the threats that could affect their assets.

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