The extensive deployment of Internet of Things (IoT) devices enables data exchange of modern objects across our everyday lives, as a result, it introduces new use cases in smart cities, smart homes, smart grids, health, cyber-physical systems (CPS), and industrial IoT (IIoT). Thanks to the rise of Industry 4.0, which facilitates IoT developments by employing intelligent, interconnected cyber-physical systems to automate industrial operations. It is worth mentioning that IIoT improves efficiency, reliability, and economical benefits through IoT-enabled CPS however, it also introduces new challenges regarding security aspects by increasing the attack landscape. The complexity of an IIoT system also makes the efforts to secure the system harder. This is mainly because in IIOT significant amount of (heterogeneous) information is gathered from different sources, and each of them requires specific techniques and solutions.
IIoT can also be considered as a critical building block for the agri-food supply chain due to its pervasiveness, interoperability, and scalability. The key building blocks of a smart agri-food supply chain are as IoT and blockchain. Attacks in the IIoT may result in abnormal behaviour in the system, which may compromise physical security, damage equipment as well as can cause financial losses. Due to the distributed nature and size of IIoT networks, conventional cryptographic / security solutions may not be beneficial. Therefore, self-aware approaches are required to protect the distributed infrastructure from malicious network attacks and unauthorized access. Contemporary Artificial Intelligence (AI) techniques, such as deep learning, machine learning, and reinforcement learning, have already been found to be useful in detecting anomalous patterns from a large amount of data and, thus, capable of employing self-awareness to identify cyber-attacks from the captured data.
To enhance the knowledge of current IoT engineers and future IoT users, there is a need to summarize the state-of-the-art techniques and possible challenges in IIoT security. This handbook will focus on cutting-edge research from both academia and industry with an emphasis on the scientific and engineering techniques which employ modern AI techniques for securing IIOT. Specifically, this book welcomes two categories of papers: (1) invited articles from qualified experts, and (2) contributed papers from an open call with a list of addressed topics. Topics of interest include but are not limited to:
- Security and privacy challenges in IIOT
- Security challenges for smart city application in IIOT
- Secure Architecture for IIOT Environments
- Secure lightweight protocol for smart IIOT
- Blockchain and Security of IIOT
- Fog/Cloud-based secure AI architecture for IIOT
- AI for securing the IoT Healthcare
- AI-based smart and secure logistic system in IIOT
- AI-based Thread detection for Industrial Manufacturing Units
- Machine learning technique for secure edge computing in IIOT
- Secure IIOT operation in Critical Infrastructure
- Secure IIOT in the Oil and Gas industry
- Threat mitigation in smart grids
- Attack detection in IoT-enabled Water resource system
Each paper submitted will be reviewed by editors and independent experts based on the relevance to the topics of the book and its usefulness for a wider range of audience Research novelty (e.g. new techniques) and potential impact Readability. All chapters should follow the Springer manuscript format as follows:
https://www.springer.com/gp/authors-editors/book-authors-editors/resources-guidelines/book-manuscript-guidelines/manuscript-preparation/5636
Important Dates:
- Submission deadline: 1 Dec. 2023
- Authors’ notification: 1 Jan. 2024
- Camera-ready version due: 31 Apr. 2024
- Tentative publication date: Jul. 2024
For more information, please contact Dr. Hadis Karimipour ([email protected])
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