Next-Generation Network Security: AI-Driven Threat Detection and Adaptive Defence Mechanisms

Dr. V Subrahmanyam, Dr. M. V. Siva Prasad

Abstract: The exponential growth of cyber threats, fuelled by increasing connectivity, cloud adoption, and the proliferation of Internet of Things (IoT) devices, has exposed the limitations of traditional network security approaches. Static firewalls, rule-based intrusion detection systems, and signature-based malware defences are increasingly insufficient against sophisticated, polymorphic, and zero-day attacks. This paper explores the paradigm shift toward artificial intelligence (AI)-driven threat detection and adaptive defence mechanisms that leverage machine learning, deep learning, and real-time behavioural analysis. The proposed approach integrates anomaly detection, automated response orchestration, and continuous learning models to create a proactive security posture. Experimental evaluations highlight improved detection accuracy, reduced false positives, and enhanced adaptability to evolving threats. This research underscores the importance of explainable AI (XAI), federated learning, and hybrid defence architectures for sustainable, next-generation network security.

Keywords: Network Security, Artificial Intelligence, Machine Learning, Adaptive Defence, Threat Detection, Cybersecurity, Explainable AI

How to Cite?: Dr. V Subrahmanyam, Dr. M. V. Siva Prasad, "Next-Generation Network Security: AI-Driven Threat Detection and Adaptive Defence Mechanisms", Volume 14 Issue 9, September 2025, International Journal of Science and Research (IJSR), Pages: 745-750, https://www.ijsr.net/getabstract.php?paperid=SR25916184226, DOI: https://dx.doi.org/10.21275/SR25916184226