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RTT-Based Rogue UAV Detection in IoV Networks

Authors :
Jie Chen
Ying He
Nilesh Chakraborty
Yao Chao
Sumit Mishra
Yi Pan
Jianqiang Li
Chengwen Luo
Source :
IEEE Internet of Things Journal. 9:5909-5919
Publication Year :
2022
Publisher :
Institute of Electrical and Electronics Engineers (IEEE), 2022.

Abstract

Unmanned Aerial Vehicles (UAVs) are being used in different emerging domains for accomplishing many critical tasks. However, due to the various constraints like – battery life, computational resources, etc., an UAV under a mission (M-UAV) often needs assistance from an edge/cloud server that is reachable from the M-UAV’s location. A connection between an M-UAV and edge server can be established via an access point or AP. Therefore, before sharing any sensitive information with the edge server, it is essential for an M-UAV to determine the legitimacy of the selected AP. Recently, some works in this direction indicate that a rogue UAV (R-UAV) can successfully mimic a legitimate AP for intercepting the communication channel. Hence, there should be a robust detection mechanism in place for addressing such a threat scenario. In this paper, considering one of the emerging domains – the Internet of Vehicle (IoV) networks, at first, we show that communication in the IoV networks can get benefit from the presence of M-UAVs. However, as the link between the M-UAV and edge server can be intercepted by an R-UAV, the adversary may access the sensitive information from the IoV networks. Followed by this, we propose a timing-based algorithm for identifying the presence of rogue APs (or R-UAVs) in the channel. The M-UAV executes the timing-based algorithm, and the detection method does not require any auxiliary hardware or any modification to the network protocols for meeting the objective. Supported by an extensive evaluation study, we show that without any rigid restriction on the M-UAV’s speed (e.g., by limiting it to almost static) the proposed approach significantly enhances the detection accuracy (at least by a margin of 29.7% and 16.65%) compared to the state-of-the-art methods.

Details

ISSN :
23722541
Volume :
9
Database :
OpenAIRE
Journal :
IEEE Internet of Things Journal
Accession number :
edsair.doi...........eb3eb92c6436890f6b3c8d47e3653bac
Full Text :
https://doi.org/10.1109/jiot.2021.3051293