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Cognitive IoT Collusion SSDF Attack Detection Based on FP-Growth Algorithm

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Abstract

Cognitive Internet of Things (CIoT) uses cognitive radio technology in the Internet of Things (IoT). It can effectively solve the problem of spectrum shortage and enable IoT devices to better access the network. But malicious IoT devices (MIDs) in the CIoT may launch Spectrum Sensing Data Forgery (SSDF) attacks to disrupt network performance. Especially when these MIDs collude, the consequences may be more serious. For solving these problems, This paper adopts an association rule mining algorithm to detect collusion SSDF attacks. For a large number of devices in CIoT, the FP-growth detection algorithm based on cluster division is proposed in the paper. We divide IoT devices in a certain area into sever sevices in each sub-cluster only need to send the perception report to the sub-fusion center (S-FC) to which it belongs, and execute the FP-growth detection algorithm based on cluster division in the S-FC to identify colluding malicious IoT devices (C-MIDs) and filter them out. Its sensing report, only the perception data of is finally sent to the fusion center (FC), and the FC performs data fusion on the received sensing report and makes a global decision. Simulation results show that the proposed FP-growth detection algorithm based on cluster division is superior to traditional methods in detection accuracy and execution time, and achieves better detection results for different types of collusion SSDF attacks in CIoT.

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Acknowledgements

The project was supported in part by the Fundamental project: Natural Science Foundation of Heilongjiang Province (LH2022F004) and Wuxi University Research Start-up Fund for Introduced Talents.

Funding

The study was funded by the Fundamental project: Natural Science Foundation of Heilongjiang Province (LH2022F004) and Wuxi University Research Start-up Fund for Introduced Talents.

Author information

Authors and Affiliations

  1. The College of Electronic Information Engineering, Wuxi University, Wuxi, 210044, China

    Miao Liu & Li Zhang

  2. Qinhuangdao Campus, Northeast Petroleum University, Qinhuangdao, 066044, China

    Miao Liu

  3. The College of Electronic Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Longxi Liu

  4. The College of Physics and Electronic Engineering, Northeast Petroleum University, Daqing, 163319, China

    Di Xu

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  1. Miao Liu
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Contributions

Liu Miao, Longxi Liu and Xu Di wrote the main manuscript text. All authors reviewed the manuscript.

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Correspondence to Miao Liu.

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Liu, M., Liu, L., Xu, D. et al. Cognitive IoT Collusion SSDF Attack Detection Based on FP-Growth Algorithm. J Netw Syst Manage 33, 25 (2025). https://doi.org/10.1007/s10922-025-09900-9

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