PIC-BI : Practical and intelligent combinatorial batch identification for UAV assisted IoT networks

Publication Type

Conference Proceeding Article

Publication Date

10-2024

Abstract

Unmanned Aerial Vehicle (UAV)-assisted IoT networks are receiving a lot of attention in academia and industry. For instance, a UAV can fly and hover over sensors, during which time the sensors simultaneously initiate batch access requests to the UAV. Typically, UAV employs batch authentication to efficiently handle these batch accesses. However, an attacker can initiate illegal requests, causing batch authentication to fail. There are various batch identification algorithms to find illegal requests, enabling legitimate sensors to establish service connections quickly. Existing work wants to choose a suitable one based on the specific attack scenario. However, existing work assumes that the percentage r% of illegal requests is known in advance, which is impractical in real-world scenarios. Besides, existing work only selects a suitable batch identification algorithm based on r%, limiting the performance of batch identification to the capabilities of the alternative algorithms. Drawing inspiration from the Kalman filter, we first propose an adaptive estimation algorithm for the number of illegal requests to address the above problems. Based on the estimated value e%, we design a combinatorial batch identification using reinforcement learning. This approach allows the combination of different algorithms to achieve superior performance. Extensive experiments demonstrate that, for the estimation algorithm, the relative error is less than 20% in 27 out of 40 experiments. Regarding the combinatorial algorithms, the delay can be reduced by approximately 7.15% to 30.86% compared to existing methods.

Keywords

UAV assisted IoT networks, Combinatorial framework, Batch identification, Kalman filter, Reinforcement learning, Mobile and wireless security

Discipline

Digital Communications and Networking | Information Security

Publication

Proceedings of the ACM Conference on Computer and Communications Security (CCS 2024) : Salt Lake City, USA, October 14-18

First Page

3645

Last Page

3658

Identifier

10.1145/3658644.3670303

Publisher

Association for Computing Machinery

City or Country

Salt Lake City, USA

Additional URL

https://doi.org/10.1145/3658644.3670303

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