Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures
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Abstract (arXiv)
Wireless Sensor Networks (WSNs) constitute one of the most promising third-millennium technologies and have a wide range of applications in our surrounding environment. The reason behind the vast adoption of WSNs in various applications is that they have tremendously appealing features, e.g., low production cost, low installation cost, unattended network operation, autonomous and longtime operation. WSNs have started to merge with the Internet of Things (IoT) through the introduction of Internet access capability in sensor nodes and sensing ability in Internet-connected devices. Thereby, the IoT is providing access to huge amount of data, collected by the WSNs, over the Internet. However, owing to the absence of a physical line-of-defense, i.e. there is no dedicated infrastructure such as gateways to watch and observe the flowing information in the network, security of WSNs along with IoT is of a big concern to the scientific community. Besides, recent integration and collaboration of WSNs with IoT will open new challenges and problems in terms of security. Hence, this would be a nightmare for the individuals using these systems as well as the security administrators who are managing those networks. Therefore, a detailed review of security attacks towards WSNs and IoT, along with the techniques for prevention, detection, and mitigation of those attacks are provided in this paper. In this text, attacks are categorized and treated into mainly two parts, most or all types of attacks towards WSNs and IoT are investigated under that umbrella: "Passive Attacks" and "Active Attacks". Understanding these attacks and their associated defense mechanisms will help to pave a secure path towards the proliferation and public acceptance of IoT technology.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Oct 2019.
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Citing papers per year: 2021: 5; 2022: 1; 2023: 1; 2024: 2; 2025: 3.
12 papers.
- 2021A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detectioncited by 10
- 2023Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classificationcited by 10
- 2021Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Surveycited by 9
- 2021Class-Incremental Learning for Wireless Device Identification in IoTcited by 9
- 2021Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platformcited by 4
- 2021Zero-bias Deep Neural Network for Quickest RF Signal Surveillancecited by 2
- 2022Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directionscited by 2
- 2025Trust Semantics Distillation for Collaborator Selection via Memory-Augmented Agentic AIcited by 2
- 2024Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problemscited by 0
- 2024Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networkscited by 0
- 2025Weaponizing Language Models for Cybersecurity Offensive Operations: Automating Vulnerability Assessment Report Validation; A Review Papercited by 0
- 2025A Hierarchical IDS for Zero-Day Attack Detection in Internet of Medical Things Networkscited by 0
- 2025Trust Semantics Distillation for Collaborator Selection via Memory-Augmented Agentic AI9 Sep 2025
- 2025A Hierarchical IDS for Zero-Day Attack Detection in Internet of Medical Things Networks14 Aug 2025
- 2025Weaponizing Language Models for Cybersecurity Offensive Operations: Automating Vulnerability Assessment Report Validation; A Review Paper7 May 2025
- 2024Enhancing Intrusion Detection in IoT Environments: An Advanced Ensemble Approach Using Kolmogorov-Arnold Networks28 Aug 2024
- 2024Privacy-Enhanced Training-as-a-Service for On-Device Intelligence: Concept, Architectural Scheme, and Open Problems16 Apr 2024
- 2023Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification30 Jan 2023
- 2022Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions24 Oct 2022
- 2021A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection25 Nov 2021
- 2021Explainable Artificial Intelligence for Smart City Application: A Secure and Trusted Platform31 Oct 2021
- 2021Zero-bias Deep Neural Network for Quickest RF Signal Surveillance12 Oct 2021
- 2021Class-Incremental Learning for Wireless Device Identification in IoT8 May 2021
- 2021Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey25 Jan 2021