• M. Jayavishnu
• K. Dinesh
• Dr. V. Sucharita

Phishing attacks pose a severe and persistent threat to cybersecurity, exploiting social engineering techniques to deceive users into divulging sensitive information. Traditional methods, such as blacklisting and heuristic-based approaches, have proven inadequate against the sophisticated and evolving nature of these attacks. In response, this study introduces a hybrid machine learning-based approach for phishing detection, focusing on URL features to distinguish between legitimate and malicious websites. The proposed system integrates logistic regression and random forest classifiers, leveraging their complementary strengths to enhance detection accuracy and robustness. The dataset, comprising labeled URLs from a publicly available repository, underwent extensive preprocessing, including handling missing values, label encoding, and removing duplicates. Text features were extracted using CountVectorizer, and models were trained and evaluated to assess their performance. Experimental results demonstrate that the hybrid model achieves high accuracy rates, outperforming individual classifiers. By leveraging both logistic regression and random forest classifiers, our system aims to enhance the accuracy and reliability of phishing detection. The system was trained and tested on a publicly available dataset, and the results demonstrate promising performance with high accuracy rates.

Phishing Detection, Cybersecurity, Hybrid Model, Logistic Regression, Random Forest, URL Analysis

"Phishing Detection System Through Hybrid Machine Learning Based On Urls", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.281-286, June-2024, Available :http://www.jetir.org/papers/JETIRGL06048.pdf

"Phishing Detection System Through Hybrid Machine Learning Based On Urls", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp281-286, June-2024, Available at : http://www.jetir.org/papers/JETIRGL06048.pdf