Neural network architecture for real-time server threat detection and mitigation

This article examines the integration of artificial intelligence and machine learning (AI-ML) technologies in server system security, highlighting their transformative potential in addressing evolving cybersecurity challenges. The article explores the theoretical foundations of AI-ML security models, including the shift from rule-based to adaptive systems and the core machine learning techniques applicable to security domains. A comprehensive article analysis of data acquisition and feature engineering methodologies reveals how diverse data sources and sophisticated preprocessing techniques enhance threat detection capabilities. The article further investigates training methodologies and model validation approaches specific to security applications, emphasizing the importance of supervised learning for known threats and unsupervised learning for zero-day exploit detection. The implementation aspects of AI-ML security systems are examined, focusing on architectural frameworks, latency considerations, scalability challenges, and integration with existing security infrastructure. Finally, the paper discusses current limitations and future research directions, providing insights into the evolving landscape of AI-enhanced server security and its implications for cybersecurity practices and policies.