Unveiling the Efficacy of AI-based Algorithms in Phishing Attack Detection

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Tajamul Shahzad
Kashif Aman


Phishing poses a significant challenge in an ever-evolving world. The increased usage of the Internet has resulted in the emergence of a different kind of theft referred to as cybercrime. The term cybercrime describes the act of invading privacy and illegitimately obtaining personal information using digital platform. Primarily an approach named phishing is employed, which involves the use of spoof emails or bogus websites by the attackers to get the victim's personal information like their account credentials, debit, or credit card’s number, etc. To give the brief knowledge of phishing attacks and their types of the objective of this work is to investigate various AI algorithms. Through a detail literature 14 AI algorithms which are repeatedly used for detection, and these are Random Forests, Convolutional Neural Network, Naïve Bayes, K-Nearest Neighbours algorithm, Decision Trees, long short-term memory, gated recurrent unit, Artificial Neural Network, AdaBoost, Logistic Regression, Gradient Boost, Multi-layer perceptron, Recurrent Neural Network, Extreme gradient boosting, and Support Vector Machine to detect phishing attacks. To verify the effectiveness of these algorithms an experiment is performed on two datasets. Among all the algorithms Convolutional Neural Network, Multi-layer perceptron and AdaBoost achieved more than 90% accuracy, precision and sensitivity and it was showed through results that these algorithms are very efficient and can achieve high accuracy if used to the requirements of specific scenario with proper planning. Moreover, the paper shows how different AI techniques have been employed in multiple studies to detect and address phishing attacks. Also, this paper gives a complete list of current problems with phishing attacks and ideas for future studies in this area.   

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How to Cite
Shahzad, T., & Aman, K. (2024). Unveiling the Efficacy of AI-based Algorithms in Phishing Attack Detection. Journal of Informatics and Web Engineering, 3(2), 116–133. https://doi.org/10.33093/jiwe.2024.3.2.9
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