Springer Book: AI-Enabled Threat Detection and Security Analysis for Industrial IoT
The increasing number of cybersecurity incidents in Industrial Internet of Things (IoT) IIoT stress the need to strengthen cyber resilience. Cyber attacks in the IIoT can result in anomalous behavior in the system that may compromise physical security, cause production downtimes, damaging equipment as well as ensuring financial and reputational losses. With the evolution of adversarial techniques, current threats become even more complicated. Therefore, intelligent approaches are required to protect the infrastructure from malicious network attacks and unauthorized access. Artificial Intelligence (AI) techniques, such as deep learning, machine learning, and reinforcement learning, have proven to be beneficial in learning the anomalous pattern from data to detect cyber attacks and reduce the workload of analysts.
To inform current IoT engineers and the future IoT players, materials summarizing the state-of-the-art techniques and possible challenges in IoT security are required. This handbook focuses on cutting-edge research from both academia and industry, with an emphasis on the scientific foundations and engineering techniques for securing critical IoT-enabled infrastructure and their underlying computing and communicating systems. Utility networks, transportation systems, and wireless communication and sensor networks are examples of such infrastructures. Providing a fundamental and theoretical background with a clear, comprehensive overview of security issues in IoT is useful for students in the fields of information technology, computer science/engineering, or electrical engineering. 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:
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Machine learning and decision automation in IIoT
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Deep learning-based cyber-attack detection for intelligent IIoT
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Vulnerability analysis and reverse engineering for IIoT
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Intrusion detection and prevention for IIoT
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Cyber threat intelligence techniques for IIoT
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Anomaly detection in IIoT and mitigation techniques
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IoT security visualization techniques for IIoT
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Collaborative intrusion detection for IoT–enabled critical infrastructure
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Adversarial deep learning for threat intelligence in IIoT
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Privacy-preserving anomaly detection in IIoT
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Machine learning aided channel identification and estimation in IIoT
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 wider range of audience Research novelty (e.g. new techniques) and potential impact Readability. All chapter 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. 2020
● Authors’ notification: 1 Jan. 2021
● Camera-ready version due: 31 Apr. 2021
● Tentative publication date: Jul. 2021
For more information please contact Dr. Hadis Karimipour (hkarimi@uoguelph.ca)