Acknowledgment to the Reviewers of Journal of Environmental & Earth Sciences in 2025
2026-02-06
Environmental IoT under Attack: A Review of Adversarial Machine Learning in Monitoring Networks
Authors
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Hui Zeng
School of AI Engineering, Guangzhou College of Technology and Business, Guangzhou 510850, China
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Massudi Mahmuddin
School of Computing, Universiti Utara Malaysia, Sintok 06010, Malaysia
DOI:
https://doi.org/10.30564/jees.v8i6.13235Abstract
Environmental Internet of Things (E-IoT) networks are also used in real-time air, water, soil, and ecological systems monitoring, which provides high-resolution data that is essential in environmental management and policy decision-making. When used in these networks, the implementation of machine learning (ML) will improve the predictive potential, anomaly detection, and automated decision-making. Nevertheless, the use of ML models brings with it additional security threats in the guise of adversarial machine learning (AML) attacks, which can alter the input to models, their training data, or parameters in order to trigger inaccurate predictions or to prevent the detection of events of interest. This review essentially offers a thorough analysis of E-IoT network AML threats, the distinct characteristics of resource-constrained, distributed, and dynamic environmental sensing environments. Along with the mechanisms, the attack surface, and the impacts that such attacks may have on monitoring reliability and decision-making, we classify AML attacks into evasion, poisoning, model extraction, and backdoor attacks. The review also provides a survey of defense techniques at data, model, and system levels, such as preprocessing, robust modeling, adversarial training, secure aggregation, and self-healing networks, and the advantages, weaknesses, and trade-offs of these techniques. Lastly, the challenges that are recognized as open include a lack of realistic datasets, coping with concept drift, resource limitations, and interdisciplinary research. Through the synthesis of existing information, this review will inform the development of resilient, safe, and reliable E-IoT networks that will be able to support reliable environmental monitoring when subjected to adversarial attacks.
Keywords:
Environmental IoT; Adversarial Machine Learning; Cybersecurity; Sensor Networks; Resilient MonitoringReferences
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Zeng, H., & Mahmuddin, M. (2026). Environmental IoT under Attack: A Review of Adversarial Machine Learning in Monitoring Networks. Journal of Environmental & Earth Sciences, 8(6), 89–103. https://doi.org/10.30564/jees.v8i6.13235
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Copyright © 2026 Hui Zeng, Massudi Mahmuddin
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
