Abstract
The increasing dependence on Wi-Fi for device-to-device communication in industrial environments has introduced significant security and privacy challenges. In such wireless networks, rogue access point (RAP) attacks have become more common, exploiting the openness of wireless communication to intercept sensitive operational data, compromise privacy, and disrupt industrial processes. Existing mitigation schemes often rely on dedicated hardware and cryptographic methods for authentication, which are computationally expensive and impractical for the diverse and resource-limited devices commonly found in industrial networks. To address these challenges, this paper introduces a robust and lightweight Wi-Fi device identification scheme, named the P-A scheme, specifically designed for industrial settings. By extracting hardware fingerprints from the phase and amplitude characteristics of channel state information (CSI), the P-A scheme offers an efficient and scalable solution for identifying devices and detecting rogue access points. A lightweight neural network ensures fast and accurate identification, making the scheme suitable for real-time industrial applications. Extensive experiments in real-world scenarios demonstrate the effectiveness of the scheme, achieving 95% identification accuracy within 0.5 s. The P-A scheme offers a practical pathway to safeguard data integrity and privacy in complex industrial networks against evolving cyber threats.
Published Version
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