Safeguarding IoT Ecosystems from Adversarial Example Attacks: Mechanisms, Impacts, and Defense Strategies

Hao Jin

2025

Abstract

Internet of Things (IoT) devices are rapidly developing in various fields such as smart homes and healthcare. However, IoT devices are highly vulnerable to adversarial example (AE) attacks. These attacks can have serious consequences, including misclassification of security systems, failure of medical diagnosis, and overall instability of the IoT ecosystem. This paper analyses AE attacks against IoT devices and explores their mechanisms, impacts, and potential defence strategies. By reviewing the existing literature and examining various mitigation techniques, including adversarial training, gradient masking, and anomaly detection, the study evaluates their effectiveness and limitations. The main findings show that while these methods provide a certain level of defence, they are not foolproof and may impose additional computational pressure or fail to defend against adaptive attacks. The study highlights the importance of developing a multi-layered security framework, integrating hybrid defence strategies, and promoting collaboration among IoT stakeholders. Future research should focus on enhancing the robustness of machine learning models through formal verification and developing effective real-time defence mechanisms to ensure the long-term security of the IoT ecosystem.

Paper Citation

in Harvard Style

Jin H. (2025). Safeguarding IoT Ecosystems from Adversarial Example Attacks: Mechanisms, Impacts, and Defense Strategies. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 560-563. DOI: 10.5220/0013702000004670

in Bibtex Style

@conference{icdse25,
author={Hao Jin},
title={Safeguarding IoT Ecosystems from Adversarial Example Attacks: Mechanisms, Impacts, and Defense Strategies},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={560-563},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013702000004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Safeguarding IoT Ecosystems from Adversarial Example Attacks: Mechanisms, Impacts, and Defense Strategies
SN - 978-989-758-765-8
AU - Jin H.
PY - 2025
SP - 560
EP - 563
DO - 10.5220/0013702000004670
PB - SciTePress