• Mr A.V Srinivas
• Rajoli Nanda Kishore Reddy
• Gubbala Hemanth Sai
• Ganta Bhushan Chandra Srinadh
• Godi Aditya Krishna Sai

Phishing attacks have become a serious cybersecurity risk, resulting in data breaches and monetary losses. In order to improve phishing detection accuracy, this work introduces a hybrid machine learning-based phishing detection system that examines URLs. The suggested model successfully classifies URLs as dangerous or valid by using many machine learning methods, including Random Forest, Decision Trees, and Support Vector Machines (SVM). Important characteristics like URL length, the presence of special characters, and domain age were collected from a dataset of more than 11,000 authentic and phishing URLs. Techniques for feature selection were used to lower computational overhead and increase classification efficiency. To maximise detection performance, the hybrid model combines the predictions of several classifiers using both soft and hard voting. Standard performance indicators, such as accuracy, precision, recall, and F1-score, were used to assess the system. According to experimental data, the suggested hybrid strategy works noticeably better than conventional detection techniques, minimising false positives while attaining more accuracy. This research adds to the continuing fight against cyber dangers by utilising cutting-edge machine learning techniques to provide a scalable and effective phishing detection solution. Deep learning methods for improved detection skills and the integration of real-time threat intelligence are examples of future study.

Phishing Detection, Machine Learning, Hybrid Model, URL Analysis, Cybersecurity, Ensemble Learning, Feature Selection, Support Vector Machine (SVM), Random Forest, Decision Tree.

"Enhanced Phishing Detection System Using Hybrid Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 3, page no.j368-j374, March-2025, Available :http://www.jetir.org/papers/JETIR2503946.pdf

"Enhanced Phishing Detection System Using Hybrid Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 3, page no. ppj368-j374, March-2025, Available at : http://www.jetir.org/papers/JETIR2503946.pdf