Securing Remote User Authentication in Industrial Internet of Things
Abstract
The Industrial Internet of Things (IIoT) enhances the benefit of the Internet of Things (IoT) to a higher level, especially in industries where human error can lead to catastrophic effects. However, security is a major concern in IIoT as hackers can gain access to connected systems, thus potentially subjecting operations to a shutdown. Besides, the outbreak of the COVID-19 pandemic changed the operations style of organizations into a remote work model. Consequently, there has been a significant increase in cyber-attacks leveraging vulnerabilities of IoT devices connected to the Internet. Considering the above factors, we propose a method of remote user authentication combining Photo Response Non-Uniformity (PRNU) with fingerprint bio-metric, which can prevent attacks. PRNU uniquely identifies the scanner, thereby authenticates the device of the user. To prove the effectiveness of PRNU, we collect fingerprint images from various scanners prototyped using Raspberry Pi and evaluate the performance. Our performance evaluation with a set of 10 fingerprint scanners shows promising results. Moreover, our analysis shows that the proposed scheme achieves a classification accuracy of 99%.
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In Prime MotorX's perception layer, weak authentication and misconfiguration in RTD user credentials are exploited by default password, DoS, and brute force attacks.
“In this scenario, the exploited vulnerabilities are weak Authentication and Misconfiguration.The launched attacks include Default Passwords attack [48], [49], DoS attack [50], [51], and Brute Force attack [52], [53], with the target being the User Credentials.”
From A Critical Overview of Industrial Internet of Things Security and Privacy Issues Using a Layer-Based Hacking Scenario (2023) · cites this paper as [48]
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Eyeleko, A. H., & Feng, T. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3308195 · Cited by 38 · see citing works
“The launched attacks include default password attack [47], [48], DoS attack [49], [50], and Brute force attack [51], [52], with the target being the user credentials.”
Background
- Authors
- Nimmy, K., Sankaran, S., Achuthan, K., & Calyam, P.
- Affiliations
- Amrita Vishwa Vidyapeetham (IN)
- University of Missouri (US)
- Venue
- 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC)
- Publisher
- IEEE
- Published
- 2022-01-08
- Pages
- 244-247
- ISSN
- 2331-9852 (ISSN-L)
- OpenAlex
- W4211037266
- Cited by
- 13
- References
- 20
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 5 · 2023: 3
- FWCI
- 2.13
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Consumer Communications and Networking Conference (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
- Physical Sciences › Computer Science › Information Systems › User Authentication and Security Systems
- Physical Sciences › Computer Science › Computer Networks and Communications › Advanced Authentication Protocols Security
- Keywords
- remote user authentication, Industrial Internet of Things, PRNU, fingerprint biometrics, device authentication, fingerprint scanners, Raspberry Pi, Internet of Things security, remote work
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