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| Title | A Survey On Device Fingerprinting Approach For Resource-Constraint Iot Devices: Comparative Study And Research Challenges |
|---|---|
| ID_Doc | 5249 |
| Authors | Chowdhury R.R.; Abas P.E. |
| Year | 2022 |
| Published | Internet of Things (Netherlands), 20 |
| DOI | http://dx.doi.org/10.1016/j.iot.2022.100632 |
| Abstract | Modernization and technological advancement have made smart and convenient living environments, including smart houses and smart cities, possible, by combining the Internet of Things (IoT), data, and internet-based services over various communication protocols. IoT is the next generation of the Internet. However, commonly resource-constraint IoT devices that are designed to perform a specific purpose, impose new security challenges, including node forgery, unauthorized access of data, and denial of services. They are more susceptible to being compromised by adversaries as opposed to general-purpose computing devices, and are exposed to different kinds of attacks, including spoofing and botnet attacks. Device identification is one of the promising approaches for improving network security. Devices can be identified either using explicit identifiers (internet protocol/media access control addresses) or implicit identifiers (network traffic and radio signal features), with implicit identifiers being more reliable, robust, and secure for device fingerprinting (DFP). In this paper, DFP methods have been studied in detail, with features generated from their communication traffic characteristics, including network traffic traces, IEEE 802.11 MAC frames, and radio signals, discussed. Additionally, key limitations and research challenges have been studied in the context of the IoT paradigm. Research challenges within the context of DFP and the future of IoT technologies are also discussed to shape future directions of work in the area. The key contribution of this study is the identification of different DFP research scopes in the domain of the IoT paradigm, which can be designed and implemented toward the development of IoT network security. © 2022 Elsevier B.V. |
| Author Keywords | Deep learning (DL); Device fingerprinting (DFP); IEEE 802.11 MAC Frames; Internet IoT things (IoT); Machine learning (ML); Network traffic traces; Radio signals |
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