TY - JOUR
T1 - On-Body Device Clustering for Security Preserving in the Internet of Things
AU - Lu, Bingxian
AU - Wang, Lei
AU - Wang, Wei
AU - Yu, Keping
AU - Garg, Sahil
AU - Piran, Md Jalil
AU - Alamri, Atif
N1 - Publisher Copyright:
IEEE
PY - 2021
Y1 - 2021
N2 - The ability to detect which wireless devices are belonging to the same person from Wi-Fi access point (AP) enables many potential Internet of Things (IoT) applications, including continuous authentication and user-oriented devices isolation. The existing cryptographic-based solutions are not suitable for IoT devices with limited power and computing capabilities. The development of electronics and chip technology makes it possible to deploy machine learning (ML) algorithms on APs. In this paper, we propose an on-body device clustering () scheme. First, the extracts the trajectory and gait patterns from wireless signals when the user is moving. Second, it utilizes a hierarchical clustering algorithm to measure the similarity of wireless signal patterns between devices. Finally, if the devices are clustered into the same cluster, they are considered to be carried by the same person. Our real-world experimental results show that the devices from about 90% of users can be clustered correctly, while maintaining the devices from only 0.7% of users may be clustered into the same cluster with others’ devices incorrectly.
AB - The ability to detect which wireless devices are belonging to the same person from Wi-Fi access point (AP) enables many potential Internet of Things (IoT) applications, including continuous authentication and user-oriented devices isolation. The existing cryptographic-based solutions are not suitable for IoT devices with limited power and computing capabilities. The development of electronics and chip technology makes it possible to deploy machine learning (ML) algorithms on APs. In this paper, we propose an on-body device clustering () scheme. First, the extracts the trajectory and gait patterns from wireless signals when the user is moving. Second, it utilizes a hierarchical clustering algorithm to measure the similarity of wireless signal patterns between devices. Finally, if the devices are clustered into the same cluster, they are considered to be carried by the same person. Our real-world experimental results show that the devices from about 90% of users can be clustered correctly, while maintaining the devices from only 0.7% of users may be clustered into the same cluster with others’ devices incorrectly.
KW - Authentication
KW - Clustering
KW - Communication system security
KW - Internet of Things
KW - Internet of Things (IoT)
KW - Machine Learning (ML)
KW - Security.
KW - Trajectory
KW - Wireless communication
KW - Wireless fidelity
KW - Wireless sensor networks
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U2 - 10.1109/JIOT.2021.3111041
DO - 10.1109/JIOT.2021.3111041
M3 - Article
AN - SCOPUS:85114739493
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
SN - 2327-4662
ER -