Guest Editors
Dr Raouf Abozariba
Email: raouf.abozariba@bcu.ac.uk
Affiliation: College of Computing, Birmingham City University, B4 7RQ, Birmingham, United Kingdom
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Research Interests: blockchain security, networked systems, 5G/6G communications, wireless security, IoT integration within enterprise and smart city, network monitoring
Dr Mohamed Ben Farah
Email: Mohamed.BenFarah@bcu.ac.uk
Affiliation: College of Computing, Birmingham City University, Birmingham, B4 7RQ, UK/West Midland
Homepage:
Research Interests: blockchain security, networked systems, smart city, encryption, metaheuristic and chaos theory
Dr Berna Bulut Cebecioglu
Email: Berna.Bulut@bcu.ac.uk
Affiliation: College of Computing, Birmingham City University, B4 7RQ, Birmingham, United Kingdom
Homepage:
Research Interests: openRAN networks, network measurements, AI for wireless networks and security, simulation and digital twin, V2X communications
Summary
The rapid growth of the Internet of Things (IoT) has transformed modern life, enabling unprecedented intelligence and automation across homes, healthcare, industry, and critical infrastructure. However, this expansion has introduced a vast attack surface, and despite concurrent security advances, cyberattacks continue to achieve profitable success rates across IoT devices and enabling networks. Cyberattacks on IoT ecosystems range from recruiting devices for botnet creation to data breaches involving private information, such as health data. The solution space spans lightweight security mechanisms (cryptography, authentication, secure updates), resilient architectures (segmentation, edge gateways, zero-trust), and ecosystem-wide governance (standards, certification, vendor accountability). Yet, existing intrusion detection solutions for IoT continue to face challenges such as resource overhead, poor generalization across devices and protocols, high false alarm rates, and lack of adaptability to evolving threats among other challenges. Developing adaptive and efficient intrusion detection systems (IDS) has therefore become a crucial research priority. This Special Issue aims to bring together novel research and recent advances in IoT intrusion detection. We welcome contributions that address theoretical foundations, innovative algorithms, resource-efficient implementations, and real-world applications. The scope includes both fundamental research and practical deployments that strengthen IoT security through robust and trustworthy IDS solutions.
Suggested Themes:
·Lightweight and resource-efficient IDS for constrained IoT devices
·Distributed and collaborative approaches for IoT intrusion detection
·Adaptive and resilient IDS architectures for evolving threat landscapes
·Edge- and fog-based IDS for real-time threat detection
·Cross-protocol and cross-domain IDS solutions
·Hybrid security techniques and domain-specific applications (smart healthcare, industrial IoT, vehicular IoT)
Keywords
intrusion detection systems (IDS), internet of things (IoT) security, machine learning (ML), deep learning, edge computing, fog computing, federated learning, lightweight security, resource-constrained devices, adversarial machine learning, explainable AI (XAI), real-time threat detection
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