Guest Editors
Thippa Reddy Gadekallu, Professor, Division of Research and Development, Lovely Professional University, Phagwara, India; Center of Research Impact and Outcome, Chitkara University, Punjab, India.
Nancy Victor, Assistant Professor, Vellore Institute of Technology, India.
Summary
The integration of Internet of Things (IoT) and Machine Learning (ML) has catalysed a paradigm shift in various domains ranging from healthcare and smart cities to industrial automation. A large number of IoT devices such as sensors and actuators are being deployed at various sites in order to collect vital data. Similarly, ML has proven noteworthy results in converting varied and intricate datasets into intelligible results and practical insights. The analytical power of ML algorithms combined with the IoT power in collecting data from multiple sources help in the development of intelligent systems, thereby making significant advancements in predictive analytics, autonomous decision making and a lot more. However, this amalgamation is not without its challenges. Data management, interoperability, resource constraints, security and privacy are some of the significant challenges associated with the same, out of which security and privacy are of paramount importance in the context of IoT, as the data collected from various sensors may be highly sensitive. Ensuring IoT security and privacy helps in protecting sensitive data, thus guaranteeing the integrity and availability of services, maintaining consumer trust, complying with regulations and mitigating financial and operational risks. Implementing strong security mechanisms and privacy measures facilitate the safe and widespread adoption of IoT technologies, promoting innovation and thereby improving the overall quality of life. To this end, the IoT-ML integration could potentially give rise to the next technological paradigm shift. This special issue welcomes state-of-the-art research that offers both theoretical and practical solutions in the realm of Machine Learning for privacy and security in IoT.
Keywords
• ML-based security and privacy solutions for IoT
• Federated Learning for distributed IoT systems
• ML-based Intrusion Detection Systems for IoT devices
• IoT architectures and protocols for preserving security and privacy
• Ethical considerations in deploying IoT-ML security and privacy solutions
• IoT forensic science
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