Green Intrusion Detection Systems for IoT (Intelligent Data-Centric Systems) - Softcover

Über die Autorinnen und Autoren

Saeid Jamshidi earned a Ph.D. in Software Engineering from Polytechnique Montréal. He also earned a bachelor’s degree in software engineering and a master’s in computer networks from Islamic Azad University, where his work focused on improving IoT communication security. An expert in IoT security, cybersecurity, edge management security, DRL, ML, and sustainable system design, he leverages his expertise to address complex challenges in secure, efficient, and environmentally responsible computing systems.

Nafi earned his Ph.D. in Software Engineering from the University of Saskatchewan, focusing his dissertation on the feasibility and adaptability of cross-language software development and maintenance. Prior to that, he completed his master’s degree at the University of Ottawa, specializing in Wireless ad hoc networking and Indoor Localization. His research primarily centered on IoT-related networking and device localization. His academic journey began with a bachelor’s degree in computer science and engineering, where he concentrated on Cloud Computing and ensuring Cloud Security. As a seasoned researcher and expert in Software Engineering and various Computer Science fields, he has spent the last decade applying Machine Learning, Deep Learning, Big Data Management, IoT Security, and Large Language Models' explainability and reasoning to tackle present and future challenges within his research domains. His work continues to drive advancements in these areas.

Full Professor of Software Engineering at Polytechnique Montréal, Canada CIFAR AI Chair on Trustworthy Machine Learning Software Systems, and FRQ-IVADO Research Chair on Software Quality Assurance for Machine Learning Applications. He received a Ph.D. in Software Engineering from the University of Montreal in 2011, with the Award of Excellence. He also received a CS-Can/Info-Can Outstanding Young Computer Science Researcher Prize for 2019. His research interests include software maintenance and evolution, machine learning systems engineering, cloud engineering, and dependable and trustworthy ML/AI. His work has received four ten-year Most Influential Paper (MIP) Awards, and six Best/Distinguished paper Awards. He has served on the program committees of several international conferences including ICSE, FSE, ICSM(E), SANER, MSR, and has reviewed for top international journals such as EMSE, TSC, TPAMI, TSE and TOSEM. He also served on the steering committee of SANER (chair), MSR, PROMISE, ICPC (chair), and ICSME (vice-chair). He initiated and co-organized the Software Engineering for Machine Learning Applications (SEMLA) symposium and the RELENG (Release Engineering) workshop series. He is co-founder of the NSERC CREATE SE4AI: A Training Program on the Development, Deployment, and Servicing of Artificial Intelligence-based Software Systems, and one of the Principal Investigators of the Dependable Explainable Learning (DEEL) project. He is on the editorial board of multiple international software engineering journals and is a Senior Member of IEEE.

Amin Nikanjam is a staff researcher at Huawei Canada. He is investigating 1) how Software Engineering practices (like testing and fault localization) can be leveraged into machine-learning software Systems and 2) how machine-learning techniques can be applied to safety-critical systems in terms of reliability, robustness, and explainability. He received his master’s and Ph.D. in Artificial Intelligence from Iran University of Science and Technology, Iran, and his bachelor’s in software engineering from the University of Isfahan. Before joining Huawei, he was a research associate at Polytechnique Montréal, an Invited Researcher at the University of Montréal, and an Assistant Professor at K. N. Toosi University of Technology, Iran. His research interests include Software Engineering for Machine Learning, Machine Learning Systems Engineering, Large Language Models for SE, and Multi-Agent Systems.

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Green Intrusion Detection Systems for IoT tackles the pressing security challenges posed by the rapid expansion of the Internet of Things (IoT). Traditional Intrusion Detection Systems (IDS) often fail to meet the unique demands of IoT networks, which are characterized by resource constraints, scalability issues, and power consumption concerns. This book introduces sustainable and energy-efficient IDS frameworks specifically designed for IoT environments. It delves into innovative lightweight security models and energy-aware IDS mechanisms that strike a balance between security efficacy, computational efficiency, and environmental sustainability. The book is structured to provide a comprehensive understanding of IDS in IoT contexts. Chapter 1 sets the stage by discussing the transformative role of IoT and the need for sustainable security solutions. Chapter 2 offers a foundational overview of IDS, highlighting the distinctions between traditional and Green IDS. Chapter 3 focuses on lightweight security models essential for resource-constrained IoT devices, while Chapter 4 delves into energy-efficient network designs. Chapter 5 explores green IDS mechanisms, including machine learning and distributed approaches. Chapter 6 addresses IoT vulnerabilities and mitigation strategies. Chapter 7 provides practical examples of sustainable IDS in various smart environments. Chapter 8 consolidates real-world case studies, and Chapter 9 discusses future directions in sustainable IoT security. The book concludes with actionable recommendations aligning technological advancements with global sustainability goals. “Green Intrusion Detection Systems for IoT” offers invaluable insights for academics and researchers in cybersecurity, IoT, and sustainability, as well as industry professionals developing IoT systems. By bridging the gap between security and sustainability, this book serves as a crucial resource for those seeking to develop and implement advanced IDS solutions tailored for next-generation IoT infrastructures.