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<p>The rise of Internet of Things (IoT) devices needs stronger protection against hacking and physical tampering. This study explores a light-based sensor built into a special photonic crystal fiber (PCF) to spot tiny physical changes in IoT systems, like unauthorized access or damaged hardware. The sensor works by tracking how light interacts with the fiber’s structure, offering fast, reliable alerts before cyberattacks happen. Such optical sensors add a vital layer of security to IoT networks, working alongside traditional methods to block threats early. This study aims to enhance cybersecurity for IoT-enabled devices by developing a high-sensitivity optical sensor. We propose a novel optical fiber design with a circular geometry, engineered to exploit surface plasmon resonance (SPR) for precise detection of physical and cyber anomalies in IoT networks. The sensing performance of the proposed design was analyzed through finite element method (FEM)-based numerical simulations. Silica serves as the foundational material, paired with a 50-nm-thick gold layer to induce SPR. Active plasmonic materials are essential for electron resonance phenomena, and gold was selected for its unmatched plasmonic activity and superior resonance efficiency compared to alternative metals, ensuring optimal sensitivity in detecting threats. The sensor achieves a peak wavelength sensitivity (WS) of&#xa0;22,277.65&#xa0;nm/RIU&#xa0;at a refractive index (RI) of&#xa0;1.40, alongside critical performance metrics including birefringence of&#xa0;2.21 × 10 − 3, coupling length 1.39 × 103&#xa0;μm, transmittance of − 35.36&#xa0;dB, and a power spectrum of&#xa0;1&#xa0;dB/m in the RI analytes range 1.38–1.42. Combining ultrahigh sensitivity, a compact design, and tunable operational capabilities, this sensor demonstrates exceptional potential for securing IoT devices by detecting subtle physical-layer anomalies linked to cyberattacks, offering a robust defense mechanism in interconnected networks.&#xa0;</p>

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Plasmonic PCF Sensor for IoT Cybersecurity: Numerical Investigation of High-Sensitivity SPR-Based Anomaly Detection

  • 期刊论文

  • 发表日期：

    2025年5月22日

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  • Sonia Akter,
  • Hasan Abdullah

摘要

The rise of Internet of Things (IoT) devices needs stronger protection against hacking and physical tampering. This study explores a light-based sensor built into a special photonic crystal fiber (PCF) to spot tiny physical changes in IoT systems, like unauthorized access or damaged hardware. The sensor works by tracking how light interacts with the fiber’s structure, offering fast, reliable alerts before cyberattacks happen. Such optical sensors add a vital layer of security to IoT networks, working alongside traditional methods to block threats early. This study aims to enhance cybersecurity for IoT-enabled devices by developing a high-sensitivity optical sensor. We propose a novel optical fiber design with a circular geometry, engineered to exploit surface plasmon resonance (SPR) for precise detection of physical and cyber anomalies in IoT networks. The sensing performance of the proposed design was analyzed through finite element method (FEM)-based numerical simulations. Silica serves as the foundational material, paired with a 50-nm-thick gold layer to induce SPR. Active plasmonic materials are essential for electron resonance phenomena, and gold was selected for its unmatched plasmonic activity and superior resonance efficiency compared to alternative metals, ensuring optimal sensitivity in detecting threats. The sensor achieves a peak wavelength sensitivity (WS) of 22,277.65 nm/RIU at a refractive index (RI) of 1.40, alongside critical performance metrics including birefringence of 2.21 × 10 − 3, coupling length 1.39 × 103 μm, transmittance of − 35.36 dB, and a power spectrum of 1 dB/m in the RI analytes range 1.38–1.42. Combining ultrahigh sensitivity, a compact design, and tunable operational capabilities, this sensor demonstrates exceptional potential for securing IoT devices by detecting subtle physical-layer anomalies linked to cyberattacks, offering a robust defense mechanism in interconnected networks.