Behavioral fingerprinting of Internet‐of‐Things devices

Bezawada, Bruhadeshwar; Ray, Indrakshi; Ray, Indrajit

doi:10.1002/widm.1337

Title: Behavioral fingerprinting of Internet‐of‐Things devices

Journal Article · 30 September 2019 · WIREs Data Mining and Knowledge Discovery

DOI: https://doi.org/10.1002/widm.1337 · OSTI ID:1567895

Bezawada, Bruhadeshwar ^[1];

^[1]; Ray, Indrajit ^[2]

Department of Computer Science Colorado State University Fort Collins Colorado
Department of Computer Science Colorado State University Fort Collins Colorado, National Science Foundation Alexandria Virginia

Abstract Rapid advances in the Internet‐of‐Things (IoT) domain have led to the development of several useful and interesting devices that have enhanced the quality of home living and industrial automation. The vulnerabilities in the IoT devices have rendered them susceptible to compromise and forgery. The problem of device authentication, that is, the question of whether a device's identity is what it claims to be, is still an open problem. Device fingerprinting seems to be a promising authentication mechanism. Device fingerprinting profiles a device based on information available about the device and generate a robust, verifiable and unique identity for the device. Existing approaches for device fingerprinting may not be feasible or cost‐effective for the IoT domain due to the resource constraints and heterogeneity of the IoT devices. Due to resource and cost constraints, behavioral fingerprinting provides promising directions for fingerprinting IoT devices. Behavioral fingerprinting allows security researchers to understand the behavioral profile of a device and to establish some guidelines regarding the device operations. In this article, we discuss existing approaches for behavioral fingerprinting of devices in general and evaluate their applicability for IoT devices. Furthermore, we discuss potential approaches for fingerprinting IoT devices and give an overview of some of the preliminary attempts to fingerprint IoT devices. We conclude by highlighting the future research directions for fingerprinting in the IoT domain. This article is categorized under: Application Areas > Science and Technology Application Areas > Internet Technologies > Machine Learning Application Areas > Industry Specific Applications

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Sponsoring Organization:: USDOE

Grant/Contract Number:: DE‐NE0008571

OSTI ID:: 1567895

Journal Information:: WIREs Data Mining and Knowledge Discovery, Journal Name: WIREs Data Mining and Knowledge Discovery Vol. 11 Journal Issue: 1; ISSN 1942-4787

Publisher:: Wiley Blackwell (John Wiley & Sons)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 5 works

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