AI Article Synopsis

  • A new technique for generating and reading digital signatures of fiber-optic networks and devices enhances their security against both physical and cyber attacks.
  • The method utilizes optical physical unclonable functions (OPUFs), specifically Rayleigh backscattering signals (RBS), which provide inherent anti-counterfeiting measures without the need for extra fabrication.
  • The study confirms the robustness and unpredictability of these digital signatures against attacks, demonstrating their effectiveness in security-related applications such as authentication and monitoring.

Article Abstract

We introduce a technique to generate and read the digital signature of the networks, channels, and optical devices that possess the fiber-optic pigtails to enhance physical layer security (PLS). Attributing a signature to the networks or devices eases the identification and authentication of networks and systems thus reducing their vulnerability to physical and digital attacks. The signatures are generated using an optical physical unclonable function (OPUF). Considering that OPUFs are established as the most potent anti-counterfeiting tool, the created signatures are robust against malicious attacks such as tampering and cyber attacks. We investigate Rayleigh backscattering signal (RBS) as a strong OPUF to generate reliable signatures. Contrary to other OPUFs that must be fabricated, the RBS-based OPUF is an inherent feature of fibers and can be easily obtained using optical frequency domain reflectometry (OFDR). We evaluate the security of the generated signatures in terms of their robustness against prediction and cloning. We demonstrate the robustness of signatures against digital and physical attacks confirming the unpredictability and unclonability features of the generated signatures. We explore signature cyber security by considering the random structure of the produced signatures. To demonstrate signature reproducibility through repeated measurements, we simulate the signature of a system by adding a random Gaussian white noise to the signal. This model is proposed to address services including security, authentication, identification, and monitoring.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10256015PMC
http://dx.doi.org/10.3390/s23115269DOI Listing

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