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Commodity ultra-high-frequency (UHF) RFID authentication systems only provide weak user authentication, as RFID tags can be easily stolen, lost, or cloned by attackers. This paper presents the design and evaluation of SmartRFID, a novel UHF RFID authentication system to promote commodity crypto-less UHF RFID tags for security-sensitive applications. SmartRFID explores extremely popular smart devices and requires a legitimate user to enroll his smart device along with his RFID tag. Besides authenticating the RFID tag as usual, SmartRFID verifies whether the user simultaneously possesses the associated smart device with both feature-based machine learning and deep learning techniques. The user is considered authentic if and only if passing the dual verifications. Comprehensive user experiments on commodity smartwatches and RFID devices confirmed the high security and usability of SmartRFID. In particular, SmartRFID achieves a true acceptance rate of above 97.5% and a false acceptance rate of less than 0.7% based on deep learning. In addition, SmartRFID can achieve an average authentication latency of less than 2.21s, which is comparable to inputting a PIN on a door keypad or smartphone.
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ActID: An efficient framework for activity sensor based user identification
Identification is the core of any authentication protocol design as the purpose of the authentication is to verify the user’s identity. The efficient establishment and verification of identity remain a big challenge. Recently, biometrics-based identification algorithms gained popularity as a means of identifying individuals using their unique biological characteristics. In this paper, we propose a novel and efficient identification framework, ActID, which can identify a user based on his/her hand motion while walking. ActID not only selects a set of high-quality features based on Optimal Feature Evaluation and Selection and Correlation-based Feature Selection algorithms but also includes a novel sliding window based voting classifier. Therefore, it achieves several important design goals for gait authentication based on resource-constrained devices, including lightweight and real-time classification, high identification accuracy, a minimum number of sensors, and a minimum amount of data collected. Performance evaluation shows that ActID is cost-effective and easily deployable, satisfies real-time requirements, and achieves a high identification accuracy of 100%.
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- Award ID(s):
- 1723596
- PAR ID:
- 10308876
- Date Published:
- Journal Name:
- Computers security
- Volume:
- 108
- ISSN:
- 0167-4048
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/https://doi.org/10.1016/j.cose.2021.102319
