The rapid proliferation of 5G-enabled IoT networks has significantly increased cybersecurity vulnerabilities, necessitating the development of advanced intrusion detection mechanisms. Traditional security solutions struggle to address the dynamic and complex nature of cyber threats targeting resource-constrained IoT devices. To overcome these challenges, the integration of AI and blockchain technology presents a novel approach to enhancing real-time intrusion detection and threat mitigation. AI-driven intrusion detection systems (IDS) leverage deep learning and machine learning models to analyze network anomalies, while blockchain ensures data integrity, decentralization, and tamper resistance. The implementation of AI-Blockchain IDS faces critical challenges, including scalability, computational overhead, interoperability, and adaptability to evolving cyber threats. This book chapter explores the convergence of AI and blockchain for secure intrusion detection in 5G-IoT environments, addressing key challenges, deployment strategies, and future research directions. The discussion highlights the role of Edge AI in enabling real-time threat detection for resource-constrained IoT devices and examines the potential of federated learning for decentralized security. Additionally, transfer learning techniques are explored to enhance the adaptability of deep learning-based IDS. Case studies on Edge AI-powered IDS deployment in smart grids, healthcare, and industrial IoT networks demonstrate the effectiveness of these technologies in mitigating cyber risks. The chapter also presents a comparative analysis of various machine learning algorithms for intrusion detection, evaluating their efficiency in securing 5G-IoT infrastructures. By addressing existing research gaps and proposing innovative solutions, this work contributes to the advancement of AI-driven secure communication and anomaly detection frameworks in next-generation IoT networks.
The rapid deployment of 5G-enabled IoT networks has transformed modern communication systems, offering ultra-low latency, massive device connectivity, and high-speed data transmission [1,2]. These advancements have enabled critical applications such as smart cities, industrial automation, healthcare monitoring, and autonomous transportation systems [3]. IoT devices continue to proliferate across interconnected environments, the attack surface for cyber threats has expanded exponentially [4]. Security breaches, unauthorized data access, and network intrusions have become significant concerns, threatening the confidentiality, integrity, and availability of sensitive information. Traditional security mechanisms, such as firewalls and signature-based intrusion detection systems (IDS), struggle to provide adequate protection due to the heterogeneous nature of IoT devices and the dynamic nature of cyberattacks [5]. Consequently, there was a growing need for intelligent and adaptive security solutions capable of mitigating emerging threats in real time [6].
AI has emerged as a promising approach to enhance intrusion detection in complex 5G-IoT environments [7]. AI-driven IDS leverage machine learning and deep learning techniques to analyze vast amounts of network traffic data, detect anomalies, and identify malicious activities with high accuracy. Unlike rule-based IDS, AI models continuously learn from evolving attack patterns, enabling proactive threat detection without extensive manual intervention [8]. Deep learning architectures, such as CNNs and recurrent neural networks (RNNs), enhance IDS capabilities by extracting high-dimensional features from network behavior. The integration of AI in intrusion detection was not without challenges [9,10]. The computational demands of deep learning models, adversarial attacks on AI algorithms, and data privacy concerns pose significant barriers to large-scale adoption [11]. Optimizing AI-driven IDS for real-time security monitoring requires innovative approaches that balance accuracy, efficiency, and resource utilization, particularly in constrained IoT environments [12].
