AI CyberShield Matrix: Intelligent Threat Detection and Analysis

Abstract: In the modern digital era, the cybersecurity threat landscape has become increasingly volatile, with sophisticated attacks such as zero-day phishing, AI-generated deepfakes, and malware bypassing traditional signature- based defenses. Comprehensive security solutions, such as Security Operations Centers (SOCs), remain prohibitively expensive and complex for individuals and small-to-medium enterprises (SMEs). To bridge this gap, this paper presents the "AI CyberShield Matrix," a unified, web-based cybersecurity toolkit powered by a Hybrid Artificial Intelligence architecture. The system integrates 14 specialized security modules into a single, user-friendly dashboard. By synergizing Supervised Learning (Random Forest) for phishing detection, Deep Learning (Convolutional Neural Networks) for deepfake analysis, and Unsupervised Learning (Isolation Forest) for User Entity Behavior Analytics (UEBA), the system provides a robust "Defense-in-Depth" mechanism. Experimental results demonstrate that this consolidated approach effectively democratizes advanced threat detection, offering real-time, highly accurate forensic analysis and remediation strategies for non-expert users.

Keywords: Cybersecurity, Hybrid Artificial Intelligence, Phishing Detection, Deepfake Analysis, UEBA, Convolutional Neural Networks (CNN), Machine Learning.