Extracted rule-based technique for anomaly detection in a global network Online publication date: Wed, 26-Oct-2022
by Nureni A. Azeez; Ogunlusi E. Victor; Sanjay Misra; Robertas Damaševičius; Rytis Maskeliunas
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 14, No. 6, 2022
Abstract: Phishing attacks deceive internet users into revealing sensitive information over the internet to the cybercriminals. The disguise as a result of phishing involves the creation of fake websites that are look-alikes of reputable websites. In this paper, a rule-based method to detect phishing attacks in a global network is presented. Four machine learning models were trained on a dataset consisting of 14 features. The machine learning algorithms used are K-nearest neighbour (kNN), support vector machine (SVM), random forest (RF), and naïve Bayes (NB). With the random forest model, a true positive of 100% and an accuracy of 98.35% were achieved. Rules were then extracted from the random forest model and embedded in a web browser extension called PhishAlert for easy application. Evaluation of the rules shows PhishAlert as an efficient tool for phishing detection. With this technique, the internet users can be easily guided and protected against cybercriminals.
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