• Iqra Fayaz
• Zahoor ahmad najar

Email phishing continues to pose a major threat to cybersecurity, re- sulting in considerable financial losses and data breaches worldwide. This dissertation offers a thorough investigation into creating a system based on machine learning for detecting phishing emails. The main aim of this research is to utilize machine learning techniques to improve the accu- racy and efficiency of phishing detection. Various algorithms, such as Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, and Logistic Regression, are utilized to classify emails as either phishing or legitimate. Hyperparameters are fine-tuned to enhance the accuracy of these algorithms, and regularization methods are employed to address overfitting issues.The performance of these models is assessed us- ing metrics such as accuracy, precision, recall, and F1-score. The findings reveal that the Random Forest algorithm, with optimized hyperparame- ters, achieves the highest detection accuracy, significantly outperforming traditional methods. This study highlights the potential of machine learn- ing in enhancing email security and provides a solid framework for future research in phishing detection. The results emphasize the necessity for continuous advancements in machine learning to protect against evolving cyber threats.

Email phishing, Machine learning, Cybersecurity, Phishing de- tection, Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, Logistic Regression, Hyperparameters, Regularization methods, Detec- tion accuracy, Precision, Recall, F1-score, Email security, Cyber threats.

"Email phishing detection using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 10, page no.c124-c137, October-2024, Available :http://www.jetir.org/papers/JETIR2410316.pdf

"Email phishing detection using machine learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 10, page no. ppc124-c137, October-2024, Available at : http://www.jetir.org/papers/JETIR2410316.pdf