Development of AI-Based Anomaly Detection Systems for Network Security

Abstract

The exponential growth of digital networks and cloud-based infrastructures has significantly increased the complexity and frequency of cyber threats. Traditional signature-based intrusion detection systems are increasingly inadequate in identifying novel, sophisticated, and zero-day attacks that deviate from known patterns. Artificial intelligence-based anomaly detection systems have emerged as a powerful alternative, capable of learning normal network behavior and identifying deviations that may indicate malicious activity. This paper presents a comprehensive, journal-ready study on the development of AI- based anomaly detection systems for network security. It examines the evolution of network threats, the limitations of conventional detection mechanisms, and the role of machine learning and deep learning models in detecting anomalous traffic patterns. Supervised, unsupervised, and hybrid learning approaches are analyzed with respect to accuracy, scalability, and adaptability to dynamic network environments. The paper also discusses feature engineering, dataset challenges, evaluation metrics, and deployment considerations in real-world networks. Experimental findings reported in recent literature demonstrate that AI-driven anomaly detection significantly improves detection rates while reducing false positives. The study concludes by outlining future research directions focused on explainable AI, federated learning, and real- time adaptive security frameworks to enhance trust and robustness in intelligent network defense systems.