Abstract :

Phishing Attacks Are Among The Most Severe Cybersecurity Threats In The Digital World. With The Exponential Increase In Cybercrimes, Traditional Security Measures Struggle To Detect And Prevent Phishing Activities Efficiently. This Study Explores A Phishing Detection System Utilizing Hybrid Machine Learning Techniques To Classify And Avoid Phishing URLs. The Research Proposes A Model Combining Decision Tree (DT), Logistic Regression (LR), And Support Vector Classifier (SVC) To Form An Ensemble Method Named LSD. The Dataset Used Consists Of 11,000+ URLs With Various Phishing And Legitimate Attributes. The Study Evaluates Performance Using Accuracy, Precision, Recall, F1-score, And Specificity Metrics. The Findings Indicate That The LSD Model Outperforms Individual Classifiers, Achieving High Accuracy And Improved Detection Rates. Future Research Should Focus On Real-time Phishing Detection And The Integration Of Advanced Deep Learning Methodologies For Enhanced Cybersecurity.

Published:

11-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

1245 - 1249

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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