A SYSTEMATIC LITERATURE REVIEW ON PHISHING DETECTION MODEL
DOI:
https://doi.org/10.24203/7pmk5z83Keywords:
phishing, algorithms, detection, social media, attackAbstract
This paper introduces a unique method using supervised learning techniques in a hybrid crime detection model to identify phishing attempts on social media sites. Effective detection systems are desperately needed given the rise in criminality on social media, especially phishing. The suggested model combines the best features of several supervised learning algorithms which comprises of random forest, decision tree, support vector machine which are frequently used in analyzing the phishing attacks, taking use of their capacity to extrapolate patterns from labeled datasets and spot questionable behavior suggestive of phishing efforts. The commonly used algorithm was Decision Tree (DT), with 14% of the total, followed by Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (12%), with 8%. The least popular algorithms were LSTM, SCS, STARMA, AUC, and FURIA, with 2% each.
Decision trees and Support Vector Machines (SVMs) are often used in phishing assault detection since they excel at classification tasks exactly what phishing detection entail. The reason for this is their ability to differentiate between trustworthy and malevolent websites or emails. Decision trees offer a clear and concise example of decision-making processes.
Decision tree (DT) presents several gaps which need to be solved, should important characteristics associated with phishing offenses be omitted or misidentified, the efficacy of the model may be jeopardized. Overfitting and class imbalance is a common problem with decision trees, particularly when working with complicated datasets. This might result in poor generalization to fresh, untested data, which would make the model less effective at identifying unusual phishing scams. Phishing statistics on social media frequently exhibit a class imbalance, with a comparatively smaller number of phishing crimes than lawful activity.
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Copyright (c) 2025 Nicholas Muriuki Muriithi, Josphat Karani
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