CHRIS: Cyber Security Hub for Responsible Intelligence Scanning

The rapid growth of digital services and interconnected systems has led to an increase in sophisticated cyber threats such as phishing, malware, network intrusions, ransomware, and deepfake-based attacks. Conventional security solutions are often limited to single-domain detection and lack adaptability, explainability, and user-centric intelligence. This paper presents CHRIS (Cyber Security Hub for Responsible Intelligence System), a unified, web-based cybersecurity platform that integrates Machine Learning (ML), Deep Learning (DL), and Generative AI to provide comprehensive and explainable threat detection. CHRIS incorporates six security modules: phishing detection, malware detection, network intrusion detection, password strength evaluation, deepfake detection, and ransomware detection, all accessible through a single interface. Random Forest, XGBoost, and Xception models are employed for predictive analysis, while Google Gemini is integrated to generate natural-language explanations, recommendations, and interactive assistance via an AI-powered chatbot. Experimental analysis demonstrates that the proposed system achieves high detection accuracy while significantly improving interpretability and usability. The results highlight the effectiveness of combining predictive security analytics with Generative AI, making CHRIS a practical and scalable solution for next-generation cybersecurity applications.