IoT Security and Privacy: Navigating Challenges, Implementing Solutions, and Harnessing Cloud/Edge/Fog Computing

The scope of this Special Issue involves the exploration of the critical domain of securing and preserving privacy within the Internet of Things (IoT) landscape. In an era rich with interconnected devices, ensuring robust security measures and safeguarding user privacy have emerged as paramount concerns. The need to strengthen security measures against evolving threats has become imperative. The interconnected nature of IoT systems introduces a myriad of vulnerabilities, ranging from insecure communication channels to compromised data integrity. Moreover, the vast volumes of sensitive data generated by IoT devices necessitate robust privacy-preserving techniques to safeguard user information from unauthorised access and exploitation.

This Special Issue aims to bring together cutting-edge research and innovative solutions that address the multifaceted challenges posed by IoT security and privacy. It explores the complexities inherent in securing the vast number of interconnected devices while maintaining user privacy across diverse IoT ecosystems. Additionally, it investigates the pivotal role played by emerging computing paradigms such as Cloud, Edge, and Fog computing in fortifying security measures and mitigating privacy risks within IoT environments. Furthermore, this Special Issue recognises the transformative potential of Machine Learning (ML) and Federated Learning (FL) to bolster IoT security and privacy. ML algorithms offer the capability to analyse vast datasets, identify patterns, and detect anomalies, thereby enhancing threat detection and prevention mechanisms within IoT systems. FL, on the other hand, enables decentralised model training across distributed IoT devices while preserving data privacy, thereby addressing concerns regarding data centralisation and privacy infringement.

Contributions to this Special Issue cover a wide range of topics including, but not limited to, the following:

1. Threats and vulnerabilities in IoT systems;
2. Privacy-preserving techniques for IoT data;
3. Blockchain for privacy and security in IoT;
4. Authentication and access control mechanisms;
5. Intrusion detection and prevention systems for IoT;
6. Compliance and regulatory frameworks for IoT security and privacy;
7. Artificial Intelligence/Machine Learning for privacy and security in IoT;
8. Federated learning techniques for decentralised IoT systems;
9. Integration of Cloud, Edge, and/or Fog computing in IoT security architectures;
10. Lightweight cryptography and ML in the IoT;
11. Zero-day attack detection and trust management in IoT;
12. Case studies and real-world applications demonstrating effective IoT security measures.

Dr. Mohammed Al-Khafajiy
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• Internet of Things (IoT)
• IoT security and privacy
• IoT threats and vulnerabilities
• privacy-preserving techniques
• cloud/edge/fog computing
• machine learning (ML)
• federated learning (FL)
• anomaly/intrusion detection