Protected Distribution Systems (PDS): Securing Communication Pathways in Intelligence Infrastructure

  • Authors

    • Yevhen Kidyaykin CEO at EKid. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2025PII3W6L

    Published 12-05-2025

  • LangChain, Protected Distribution Systems, PDS, SCIF, CNSSI 7003, ICD 705, Physical Security, Fiber-Optic Monitoring, Classified Communications, Category 1 PDS, Category 2 PDS, Distributed Acoustic Sensing

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    Y. Kidyaykin, “Protected Distribution Systems (PDS): Securing Communication Pathways in Intelligence Infrastructure”, IJETMR, vol. 8, no. 2, pp. 01–09, Dec. 2025, doi: 10.67228/30715636/IJETMR-2025PII3W6L.

  • Abstract

    Protected Distribution Systems (PDS) provide a physical security layer for safeguarding unencrypted classified and sensitive communications within government, military, and intelligence facilities. This paper analyses modern PDS architectures and their governing criteria, examining design methodology for Category 1 and Category 2 systems under CNSSI No. 7003 and the ICD 705 framework. The paper first clarifies the category and carrier-type structure of CNSSI No. 7003, which is frequently misstated in the applied literature, and situates PDS correctly within the instruction's own risk hierarchy, in which approved encryption and the establishment of a Controlled Access Area are the preferred protections and a PDS is a risk-managed alternative suited to low and medium threat environments. It then presents a threat-driven framework for risk mitigation, introduces a composite metric for evaluating PDS integrity, and proposes an adaptive monitoring architecture integrating distributed fiber-optic sensing with machine learning-based anomaly classification. The monitoring architecture is presented as a design proposal with an accompanying evaluation protocol; it has not been deployed, and no detection performance is claimed for it. The paper concludes by specifying the experimental design, dataset requirements, and reporting standards that would be required to validate the architecture.

  • References

    [1] Committee on National Security Systems, “CNSSI No. 7003: Protected Distribution Systems,” CNSS, Fort Meade, MD, 2015.

    [2] National Institute of Standards and Technology, “NIST SP 800-53 Rev. 5: Security and Privacy Controls for Information Systems,” NIST, Gaithersburg, MD, 2020.

    [3] Office of the Director of National Intelligence, “ICD 705: Sensitive Compartmented Information Facilities,” ODNI, Washington, DC, 2010.

    [4] K. Shaneman and S. Gray, "Optical network security: Technical analysis of fiber tapping mechanisms and methods for detection & prevention," in Proc. IEEE MILCOM 2004 Military Communications Conf., Monterey, CA, 2004, vol. 2, pp. 711–716, doi: 10.1109/MILCOM.2004.1494884.

    [5] M. P. Fok, Z. Wang, Y. Deng, and P. R. Prucnal, "Optical layer security in fiber-optic networks," IEEE Trans. Inf. Forensics Security, vol. 6, no. 3, pp. 725–736, Sep. 2011, doi: 10.1109/TIFS.2011.2141990.

    [6] A. Hartog, An Introduction to Distributed Optical Fibre Sensors. Boca Raton, FL: CRC Press, 2017.

    [7] K. Shimizu, T. Horiguchi, and Y. Koyamada, “Coherent OTDR with enhanced sensitivity by pulsed linear polarization modulation,” J. Lightw. Technol., vol. 39, no. 11, pp. 3451–3462, Jun. 2021.

    [8] Telecommunications Industry Association, “TIA-568.3-D: Optical Fiber Cabling and Components Standard,” TIA, Arlington, VA, 2016.

    [9] G. E. Suh and S. Devadas, "Physical unclonable functions for device authentication and secret key generation," in Proc. 44th ACM/IEEE Design Automation Conf. (DAC), San Diego, CA, 2007, pp. 9–14, doi: 10.1145/1278480.1278484.

    [10] Executive Order 13587, "Structural Reforms to Improve the Security of Classified Networks and the Responsible Sharing and Safeguarding of Classified Information," Federal Register, vol. 76, no. 198, Oct. 7, 2011.

    [11] C. Natalino, M. Schiano, A. Di Giglio, L. Wosinska, and M. Furdek, "Experimental study of machine-learning-based detection and identification of physical-layer attacks in optical networks," J. Lightw. Technol., vol. 37, no. 16, pp. 4173–4182, Aug. 2019, doi: 10.1109/JLT.2019.2923558.

    [12] K. Abdelli, J. Y. Cho, F. Azendorf, H. Griesser, C. Tropschug, and S. Pachnicke, "Machine-learning-based anomaly detection in optical fiber monitoring," J. Opt. Commun. Netw., vol. 14, no. 5, pp. 365–375, May 2022, doi: 10.1364/JOCN.451289.

    [13] M. Furdek, N. Skorin-Kapov, S. Zsigmond, and L. Wosinska, "Physical-layer security in evolving optical networks," IEEE Commun. Mag., vol. 54, no. 8, pp. 110–117, Aug. 2016, doi: 10.1109/MCOM.2016.7537185.

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