Enhancing Cybersecurity: A Scalable System for Real-Time Threat Detection Using ML and Automated Response

Advanced forensic and cybersecurity technologies are required due to the swift evolution of cyber threats. Real-time threat detection and scalability are problems for traditional solutions since these rely on antiquated technologies and human processes. These technologies are overpowered by the massive amount of data produced by contemporary IT infrastructures and are reactive, frequently detecting risks after an incident. To overcome these limitations, the proposed system combines real-time data analytics, automated incident response, and machine learning (ML). It collects information from multiple sources, preprocesses it, and then uses ML methods such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise) and Isolation Forest to identify anomalies. Timely threat mitigation is ensured via automated reactions and real-time analytics. The evaluation reveals that the proposed system handles 20 TB of data in 45 minutes as opposed to 5 TB in 60 minutes by conventional systems, increases threat detection accuracy to 92.0% from 85.0%, and decreases average response time to 10 seconds from 45 seconds. These improvements show notable improvements in cybersecurity efficacy and efficiency.