Green Intrusion Detection Systems for IoT
- 1st Edition - September 1, 2026
- Latest edition
- Authors: Saeid Jamshidi, Amin Nikanjam, Kawser Wazed Nafi, Foutse Khomh
- Language: English
Green Intrusion Detection Systems for IoT tackles the pressing security challenges posed by the rapid expansion of the Internet of Things (IoT). The book delves into innova… Read more
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Additional sections explore green IDS mechanisms, including machine learning and distributed approaches, IoT vulnerabilities and mitigation strategies, practical examples of sustainable IDS in various smart environments, real-world case studies, and future directions in sustainable IoT security. The book concludes with actionable recommendations that align technological advancements with global sustainability goals.
- Introduces energy-efficient IDS frameworks tailored for resource-constrained IoT networks
- Explores lightweight security models that optimize power and computational efficiency
- Provides real-world case studies across diverse IoT applications
- Discusses future trends in AI-driven and quantum-resistant IDS solutions
- Aligns IDS development with global sustainability goals and standards
2. Foundations of IDS for IoT
3. Lightweight Security Models for IoT
4. Energy-Efficient IoT Networks
5. Green Intrusion Detection Mechanisms
6. Addressing IoT Vulnerabilities
7. Integration of Sustainable IDS in Smart Environments
8. Case Studies and Real-World Applications
9. Future Directions in Sustainable IoT Security
10. Conclusion
- Edition: 1
- Latest edition
- Published: September 1, 2026
- Language: English
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Saeid Jamshidi
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.
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Amin Nikanjam
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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Kawser Wazed Nafi
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Foutse Khomh
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.