Guest Editors
Dr. Kamran Siddique
Email: ksiddique@alaska.edu
Affiliation: Department of Computer Science, University of Alaska Anchorage, Anchorage, AK 99508, USA
Homepage:
Research Interests: Big Data Analytics, Cyber Security, Machine Learning, Cloud Computing, System Architectures
Dr. Ka Lok Man
Email: ka.man@xjtlu.edu.cn
Affiliation: School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China, China
Homepage:
Research Interests: AI, IoT, Big Data Analytics, WSN
Summary
The convergence of the Internet of Things (IoT) and cloud computing has enabled a new generation of smart, connected environments. However, this integration also introduces critical security vulnerabilities and broadens the attack surface across both local and distributed infrastructures. Traditional security mechanisms often fall short in handling the complexity, heterogeneity, and scale of modern IoT-cloud ecosystems.
This Special Issue aims to explore secure and intelligent intrusion detection techniques tailored for IoT and cloud-integrated environments, with a focus on machine learning, anomaly detection, adaptive defense systems, and privacy-aware frameworks. We invite high-quality contributions that address both theoretical advancements and practical implementations to safeguard data, devices, and networks in dynamic and decentralized settings.
We especially welcome research that bridges gaps between AI-powered detection methods and real-world IoT-cloud deployment challenges, including energy constraints, decentralized learning, and evolving cyber threats.
Topics of Interest (include but are not limited to):
· Lightweight intrusion detection systems for edge and IoT devices
· Deep learning and reinforcement learning for cyber threat detection
· Secure cloud-based architectures for IoT systems
· Privacy-preserving detection and data aggregation
· Federated and distributed learning for security
· Adaptive and self-healing intrusion response systems
· Zero-day attack detection in smart environments
· Hybrid detection models combining rule-based and AI-driven strategies
· Benchmarking and performance evaluation of IDS in IoT-cloud frameworks
Keywords
Intrusion Detection; Internet of Things (IoT); Cloud Security; Cybersecurity; Anomaly Detection; Machine Learning; Intelligent Systems
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