Abstract

Next-generation communication networks, including 5G, beyond-5G (B5G), and emerging 6G architectures, are transforming global connectivity by enabling ultra-low latency, massive machine-type communications, and enhanced mobile broadband services. However, these advancements also introduce unprecedented cybersecurity challenges due to network softwarization, virtualization, edge computing, and the integration of heterogeneous devices. Traditional rule-based and signature-driven security mechanisms are increasingly inadequate to address the dynamic, large-scale, and intelligent cyber threats targeting these networks. As a result, artificial intelligence (AI) has emerged as a critical enabler for proactive, adaptive, and autonomous cyber defense mechanisms.

This paper presents a comprehensive study of AI-driven cyber defense mechanisms for securing next-generation communication networks. It explores how machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques are applied across different security layers to enhance threat detection, intrusion prevention, anomaly identification, and response automation. The study highlights the role of AI in securing software-defined networking (SDN), network function virtualization (NFV), network slicing, and edge intelligence, which form the backbone of modern communication infrastructures.

A systematic literature review is conducted to analyze existing research efforts, identifying current trends, strengths, and limitations of AI-based cybersecurity solutions. Building on these insights, the paper proposes a structured methodology for designing and evaluating AI-driven cyber defense frameworks tailored to next-generation networks. The methodology incorporates data acquisition, feature engineering, model training, real-time inference, and continuous learning to ensure resilience against evolving threats.

Experimental results and qualitative analysis demonstrate that AI-driven mechanisms significantly outperform traditional security approaches in terms of detection accuracy, adaptability, and response time. However, challenges such as data privacy, adversarial attacks against AI models, computational overhead, and lack of explainability remain critical concerns.

The paper concludes by discussing future research directions, emphasizing the need for explainable AI, federated learning, and standardized security benchmarks. The findings underscore the importance of AI-driven cyber defense as a foundational component for securing next-generation communication networks in an increasingly connected digital ecosystem.