Real Time Attack Detection using Machine Learning Techniques

As phishing attacks become more sophisticated, traditional detection methods struggle to keep up. Our system integrates multiple detection techniques into a unified framework, improving accuracy and resilience against zero-day threats. Unlike conventional models that focus solely on content filtering, URL analysis, or malware scanning, our approach employs an adaptive learning mechanism that continuously refines detection by incorporating real-world phishing patterns, enabling dynamic self-improvement without manual updates. A key innovation is Random Forest-based image classification, which analyzes logos, fake branding, and manipulated images to detect phishing attempts that evade text-based filters. In addition, the system examines the email content for phishing indicators, verifies URLs for suspicious characteristics, and scans attachments using hash-based methods to identify malware. By combining machine learning with heuristic analysis, it enhances detection accuracy while minimizing false positives. With a web-based interface for efficient monitoring, our system ensures proactive and precise phishing detection. Unlike previous research, our article uniquely integrates image analysis, content inspection, and URL verification simultaneously, providing a more comprehensive and robust phishing detection approach.