AI-Driven Intrusion Detection Systems For Next-Generation Networks: Techniques, Challenges And Future Directions

Abstract

The rapid evolution of next-generation networks, including 5G technology, emerging 6G technology, and large-scale Internet of Things ecosystems, has significantly increased the complexity and scale of cybersecurity threats. Traditional intrusion detection systems (IDS), primarily based on signature and rule-based techniques, are increasingly ineffective against sophisticated attacks such as zero-day exploits, polymorphic malware, and encrypted traffic anomalies. In this context, Artificial Intelligence (AI)-driven IDS has emerged as a promising solution for enhancing detection accuracy, adaptability, and real-time response capabilities. This paper presents a comprehensive analysis of AI-based intrusion detection mechanisms tailored for next-generation network environments. It explores the integration of machine learning (ML), deep learning (DL), and hybrid intelligent models for detecting both known and unknown threats. Specifically, techniques such as convolutional neural networks, recurrent neural networks, long short-term memory models, and generative adversarial networks are evaluated for their ability to capture complex traffic patterns and temporal dependencies. The study further examines widely used benchmark datasets, including NSL-KDD, UNSW-NB15, and CICIDS2017, to assess model performance across diverse attack scenarios..