The Comparative Analysis of Machine Learning Algorithms for Phishing Attack Detection | [Journal of Information & Communication Technology (JICT) • 2024]

Author(s):

1. Almina Sehrish: SZABIST University Gharo Campus Country: Pakistan

2. Muhammad Raza: SZABIST University, Gharo Campus, Pakistan

3. Muhammad Faizan Khan: IQRA University Country, Pakistan

4. Sabih Hida: SZABIST University Country Pakistan

5. Rageshwari Haryani: SZABIST University Country, Pakistan

Abstract:

Due to the internet's indispensable role in dayto-day operations, cybercrimes have increased dramatically, with phishing being a serious concern. Phishing attacks employ phony websites to obtain sensitive data and user passwords. Because hackers are always trying to change their strategies wisely, traditional preventative measures like software detection and user awareness frequently fall short. With their capacity for self-learning, machine learning-based solutions provide a more potent protection. Using a supervised learning framework, this paper offers a thorough review of machine learning techniques for phishing detection, making use of a dataset that has 87 attributes. Runtime, train accuracy, test accuracy, precision, recall, and F1-score are used to assess the efficiency of various algorithms, such as Random Forest, Decision Trees, SVM, Naive Bayes, K-Nearest Neighbors (KNN), XGBoost, Boosted Decision Tree, AdaBoost, Extra Trees, LightGBM, and CatBoost. With a 97.33% test accuracy as well as outstanding recall, precision, and F1-score, XGBoost stands out. Powerful performance is also demonstrated by Random Forest and LightGBM, demonstrating their effectiveness in identifying phishing attempts. This study feeds future research on optimization tactics and ensemble approaches to improve detection robustness and accuracy, and it gives cybersecurity professionals effective insights for better phishing detection.

Page(s): 22-30

DOI: DOI not available

Published: Journal: Journal of Information & Communication Technology (JICT), Volume: 18, Issue: 2, Year: 2024

Keywords:

Cybersecurity , Supervised Learning , URL Feature Extraction , Phishing Detection , Machine Algorithms

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