International Journal of Science, Engineering and TechnologyAuthors: Suyash Kangude, Prashant Aher, Aniket Mehare, Sanket Bhavari, Harshada Ahire, Abhishek Badadhe
Abstract: Phishing remains one of the most widespread and dangerous cyberattacks, where adversaries create deceptive domains and fraudulent websites to obtain confidential information from unsuspecting users. As phishing techniques evolve and become increasingly advanced, traditional security measures are often unable to recognize or block these threats, since attackers continually modify their strategies. This project introduces PhishEye a machine learning–based phishing domain detection system designed to identify, evaluate, and respond to phishing attempts. The framework follows a structured pipeline consisting of data collection, feature extraction, model training, real-time threat detection, takedown request automation, and dashboard monitoring. Datasets were gathered from trusted sources such as PhishTank and OpenPhish, complemented by additional feeds and domain permutations generated with Dnstwist. To create robust feature vectors, multiple attributes were included: lexical, domain, network, SSL/TLS, and webpage content features. Machine learning models—including Random Forest and Logistic Regression—were trained and validated using evaluation measures such as accuracy, precision, recall, and F1-score. Once deployed, the system monitors domains in real time and assigns a risk score (low, medium, or high) to estimate their legitimacy. For domains classified as high risk, PhishEye initiates an automated takedown request to the hosting provider, registrar, or relevant CERT authority. Finally, a web-based dashboard provides a comprehensive interface for administrators, showing real-time statistics and system performance for effective phishing defense.
