Employing Big Data and AI to Real-Time Detect Emerging Threats: Improved Machine Learning for Cyberattack Prediction

Modern society is witnessing rapid digitization, which caused the explosive increase of Cyber threats which requires advanced mechanisms for threat detection and mitigation in real time. In this paper we investigate the use of Big Data and Artificial Intelligence (AI) to make predictions on future and to detect in advance emerging cyberattacks through advanced machine learning (ML) algorithms. AI-driven solutions are becoming indispensable because traditional cybersecurity measures struggle to cope with the scale, complexity and speed that characterize many modern cyber-attacks. AI models depend on having as much data as possible, which is where Big Data comes into play: it pulls from large quantities of diverse data across numerous sources (such as raw network logs, user behavior analytics and threat intelligence feeds) to identify key patterns and features to create models. This paper proposes an optimized framework synergizing machine learning algorithms and real-time data analytics to identify quantifiable anomalies and forecast future threats accurately. Innovations include adaptive learning systems that can update in real-time to address zero-day vulnerabilities and previously unidentifiable attack vectors. Additionally, the framework includes an explainable AI (XAI) component that aims to increase transparency and trust in automated decision-making processes. Experimental results show significant result improvements in the presented detection accuracy, response times, and false-positive rates against typical systems. The results highlight the revolutionary potential of merging Big Data with AI to develop responsive, effective, and scalable cybersecurity that protects critical digital infrastructures.