Detection Of E-mail Phishing Attacks Using Machine Learning And Deep Learning: A Review
Kanchan Dhondiba Waghmare
, Prof.S.Khan
Email Phishing, Phishing, Machine Learning, Deep Learning
Email phishing remains one of the most persistent cybersecurity challenges, contributing to significant data breaches and financial damage across the globe. This dissertation presents an in-depth exploration of developing a machine-learning-driven system for identifying phishing emails. The primary objective is to apply ML techniques to improve both the precision and reliability of phishing detection. Several classification algorithms—including Support Vector Machine (SVM), Decision Tree, Naive Bayes, Random Forest, and Logistic Regression—are implemented to distinguish malicious emails from legitimate ones. To maximize performance, hyperparameters are carefully optimized, and regularization strategies are applied to reduce the risk of overfitting. Model effectiveness is evaluated using standard metrics such as accuracy, precision, recall, and F1-score. The results indicate that the Random Forest model, when properly tuned, delivers the highest detection accuracy and notably surpasses conventional approaches. This research demonstrates the strong potential of machine learning in strengthening email security and provides a comprehensive foundation for future work in phishing detection. Overall, the findings underscore the ongoing need for continuous improvements in ML techniques to defend against ever-advancing cyber threats. [2]
"Detection Of E-mail Phishing Attacks Using Machine Learning And Deep Learning: A Review", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.10, Issue 12, page no.b13-b17, December-2025, Available :https://ijsdr.org/papers/IJSDR2512104.pdf
Volume 10
Issue 12,
December-2025
Pages : b13-b17
Paper Reg. ID: IJSDR_306465
Published Paper Id: IJSDR2512104
Downloads: 000342
Research Area: Science and Technology
Country: Latur, MAHARASHTRA, India
DOI: https://doi.org/10.56975/ijsdr.v10i12.306465
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
