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

• Main Authors: REN, Zhe, LI, Xinghua, MIAO, Yinbin, ZHU, Mengyao, YUAN, Shunjie, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: UAV assisted IoT networks; Combinatorial framework; Batch identification; Kalman filter; Reinforcement learning; Mobile and wireless security; Digital Communications and Networking; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/9795
• Institution: Singapore Management University

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.