Intelligent Intrusion Detection Systems for Secure Communication in Next-Generation Networks

Abstract

As communication technologies evolve toward next-generation networks (5G and beyond), ensuring robust security has become a critical challenge. Traditional intrusion detection systems (IDS) often struggle to cope with the massive data volume, dynamic architecture, and heterogeneous nature of these networks. This research paper presents an in-depth analysis of Intelligent Intrusion Detection Systems (IIDS) that leverage Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to detect, classify, and prevent cyber threats in real-time. The study evaluates various ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), and Deep Neural Networks (DNN), using benchmark datasets like NSL-KDD and CICIDS2017. Results indicate that AI-based IDS models outperform conventional systems in terms of detection accuracy, adaptability, and latency reduction. The paper concludes by proposing a hybrid AI-driven IDS framework optimized for next-generation networks (NGNs), emphasizing scalability, automation, and low false alarm rates.