Browsing by Author "Jimoh, M. K."

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    Malicious Uniform Resource Locator Detection Using Wolf Optimization Algorithm and Random Forest Classifier
    (Machine Learning and Data Mining for Emerging Trend in Cyber Dynamics: Theories and Applications, 2021) Adewole, K. S.; Raheem, M. O.; Abikoye, O. C.; Ajiboye, A. R.; Oladele, Tinuke Omolewa; Jimoh, M. K.; Aremu, D. R.
    Within the multitude of security challenges facing the online community, malicious websites play a critical role in today’s cybersecurity threats. Malicious URLs can be delivered to users via emails, textmessages, pop-ups or advertisements. To recognize these malicious websites, blacklisting services have been created by the web security community. This method has been proven to be inefficient. This chapter proposed meta-heuristic optimization method for malicious URLs detection based on genetic algorithm (GA) and wolf optimization algorithm (WOA). Support vector machine (SVM) as well as random forest (RF) were used for classification of phishingweb pages. Experimental results showthatWOAreduced model complexity with comparable classification results without feature subset selection. RF classifier outperforms SVM based on the evaluation conducted. RF model without feature selection produced accuracy and ROC of 0.972 and 0.993, respectively, while RF model that is based onWOA optimization algorithm produced accuracy of 0.944 and ROC of 0.987. Hence, in view of the experiments conducted using two well-known phishing datasets, this research shows that WOA can produce promising results for phishing URLs detection task.