Cyber Attack Detection

The real-time cyber threat detection system presented in this paper is intended to shield users from threats posed by steganographic images, malicious files, and URLs. The system uses signature-based scanning engines like ClamAV, VirusTotal, and Google Safe Browsing instead of sophisticated machine learning algorithms. It supports both automated scanning of the Downloads folder, where newly added files are instantly examined, and manual inspection, which enables users to confirm questionable content. Users receive immediate alerts via WebSocket communication, and threats are categorized into severity levels (High, Medium, Low, and Clean) based on scan results.A built-in AI chatbot, powered by Groq’s LLAMA 3.3 model, assists users by explaining scan outcomes and offering security guidance. The system emphasizes usability, privacy, and low resource consumption, making it suitable for personal and small-scale organizational use. Future enhancements include browser extensions, mobile integration, and support for cloud storage scanning. This hybrid approach offers a scalable and accessible solution for modern cybersecurity needs