PHISHING, SPAM AND RANSOMWARE DETECTION

Abstract: With growing cybersecurity threats like phishing, spam and ransomware are increasing rapidly, causing major security risk and data loss, it is difficult to accurately detect them. This paper presents an integrated detection system which uses rule-based heuristics and machine learning models deployed within web-based platform. The system consists of modules like Phishing, URLs, SMS spam, Email spam and Ransomware Detection. The design include user authentication and login history, helps in real-time deployment. We tested the system on standard datasets and found it to be highly accurate and reliable, mainly high accuracy is seen in spam and ransomware classification. These results show that the proposed approach is both scalable and effective for real-time threat detection.

Keywords: Phishing detection, spam filtering, ransomware detection, machine learning, cybersecurity, XGBoost, Random Forest, Naïve Bayes, hybrid models.