The Comparative Analysis of Machine Learning Algorithms for Phishing Attack Detection
Volume 18, Issue 2, 2024
Download| Author(s): |
Almina Sehrish SZABIST University, Pakistan, alminasehrish88@gmail.com Muhammad Raza* SZABIST University, Gharo Campus, Pakistan, razacom_2000@yahoo.com Muhammad Faizan Khan IQRA University, Karachi, Pakistan, faizanonline86@gmail.com Sabih Hida SZABIST University, Pakistan, sabihhida@hotmail.com Rageshwari Haryani SZABIST University, Pakistan , rageshwariharyani1997@gmail.com |
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| Abstract | Due to the internet's indispensable role in day-to-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 thesis 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. |
| Keywords | Phishing Detection, Machine Learning Algorithms, Supervised Learning, URL Analysis, Feature Extraction, Cybersecurity |
| Year | 2024 |
| Volume | 18 |
| Issue | 2 |
| Type | Research paper, manuscript, article |
| Journal Name | Journal of Information & Communication Technology | Publisher Name | ILMA University | Jel Classification | - | DOI | - | ISSN no (E, Electronic) | 2075-7239 | ISSN no (P, Print) | 2415-0169 | Country | Pakistan | City | Karachi | Institution Type | University | Journal Type | Open Access | Manuscript Processing | Blind Peer Reviewed | Format | Paper Link | https://jict.ilmauniversity.edu.pk/journal/jict/18.2/4.pdf | Page | 22-30 | < /tr>