CyberGuardX (Cerberus-AI CyberShield): An Intelligent Threat Detection with Explainable AI and Real-Time Threat Response

The conventional malware detection systems are based on signature-based methods that cannot work against zero-day attacks and advanced evasion methods. The current paper describes CyberGuardX (Cerberus-AI CyberShield), a multi-modal malware detection system with a combination of a static analysis, machine learning classification, explainable artificial intelligence (XAI), and real-time monitoring functionalities. The system uses Random Forest classifiers which are trained with deep static features which are found on PE files, PDFs and document formats with 99.9% accuracy in malware detection. One of the major advances is the incorporation of SHAP (SHapley Additive explanations) as a method of transparent decision-making, which solves the problem of the black box of AI-based security systems. The framework includes VirusTotal API integration as an external threat intelligence, real-time file system monitoring, and a full web-based dashboard to support a security analyst. Performance analysis proves to be highly effective with better detection percentage more than the traditional signature-based systems having response times, less than 2.3 seconds to complete analysis processes. Its containerized deployment and scalable batch processing architecture is relevant to enterprise security operations centers (SOCs).