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P2ADF: a privacy-preserving attack detection framework in fog-IoT environment

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Abstract

In recent years, the Internet of Things (IoT) has gained much popularity, increasing the flow of sensitive user data across the web. In addition, the adoption of fog and edge technologies for latency-sensitive applications aggravates the privacy issues in the scenario as the sensitive data are processed in the user vicinity. Furthermore, the presence of the processing layer near the user end increases the attack surface and thus attracts malicious or curious intruders. In this light, the authors present a stacked-ensemble privacy-preserving attack detection framework, P2ADF. The framework detects the popular man-in-the-middle (MiTM) and denial-of-service (DoS)/distributed DoS (DDoS) attacks in the fog-IoT setup with a maximum accuracy of about 99.98 percent. The proposed model is trained over benchmark datasets, say, IoTID20, TON_IoT, N-BaIoT, UNSW-NB15, and CICDDoS19. The performance of the proposed model is also compared to existing state-of-the-art approaches, and P2ADF outperforms them all.

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Authors and Affiliations

  1. Department of Information Technology, BBA University, Lucknow, India

    Jasleen Kaur, Alka Agrawal & Raees Ahmad Khan

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  1. Jasleen Kaur
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  2. Alka Agrawal
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(1) JK made substantial contributions to the design of the work and drafted it. (2) AA revised it critically for important intellectual content; (3) RAK approved the version to be published; (4) all the authors agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Kaur, J., Agrawal, A. & Khan, R.A. P2ADF: a privacy-preserving attack detection framework in fog-IoT environment. Int. J. Inf. Secur. 22, 749–762 (2023). https://doi.org/10.1007/s10207-023-00661-7

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