AI/ML-Driven Intrusion Detection Systems for the Internet of Things: Techniques, Challenges, and Future Directions

Dear Colleagues,

The proliferation of Internet of Things (IoT) devices in various sectors, including healthcare, smart cities, transportation, and industrial automation, has brought about numerous opportunities for innovation. However, the rapid expansion of IoT networks also poses significant security risks. The unique characteristics of IoT systems—such as their heterogeneity, limited computational resources, and continuous connectivity—create challenges for traditional intrusion detection systems (IDS). As cyber-attacks become increasingly sophisticated, conventional IDS are struggling to keep up with evolving attack vectors, real-time monitoring demands, and the large-scale, distributed nature of IoT environments.

This Special Issue, “AI/ML-Driven Intrusion Detection Systems for the Internet of Things: Techniques, Challenges, and Future Directions”, aims to explore the latest research on the design, development, and deployment of IDS tailored for IoT ecosystems. It seeks to present innovative approaches for addressing the unique security challenges posed by IoT networks, particularly through the integration of artificial intelligence (AI) and machine learning (ML) techniques. We invite contributions that focus on both foundational research and practical solutions for intrusion detection in IoT contexts.

Submissions that explore the intersection of IoT security with AI, machine learning, big data analytics, and edge computing are highly encouraged. Topics of interest include, but are not limited to, anomaly detection, real-time threat monitoring, lightweight IDS for resource-constrained devices, and the application of AI for adaptive threat detection and response. We are particularly interested in work that addresses the scalability and interoperability of IDS across heterogeneous IoT networks and incorporates explainable AI (XAI) for enhancing the trustworthiness and transparency of security operations.

This Special Issue aims to provide a platform for researchers and practitioners to share novel ideas, methodologies, and practical implementations that address the current and future challenges of intrusion detection in IoT environments.

Topics of Interest

• AI- and ML-based intrusion detection systems for IoT;
• Real-time intrusion detection and monitoring in IoT environments;
• Lightweight IDS for resource-constrained IoT devices;
• Anomaly detection and behavior analysis in IoT networks;
• Edge and fog computing for IoT security;
• Scalable and adaptive IDS for large-scale IoT networks;
• Explainable AI (XAI) for intrusion detection transparency;
• Threat detection and response in smart cities and industrial IoT;
• Security analytics and big data fusion for IoT networks;
• Threat intelligence sharing and integration in IoT environments;
• Evaluation methodologies, real-world IoT security deployments, and benchmarks;
• Ethical and privacy concerns in IoT intrusion detection systems.

Prof. Dr. Md Arafatur Rahman
Prof. Dr. Prashant Pillai
Guest Editors

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• IoT security
• intrusion detection systems
• AI-based intrusion detection
• machine learning
• anomaly detection
• lightweight IDS
• real-time monitoring
• edge computing
• big data analytics
• explainable AI
• scalable security solutions
• threat detection