MIL-STD-1553 is a military standard that defines the electrical and protocol characteristics for a data bus used for communication between various avionics systems on military aircraft and spacecraft. However, this standard lacks built-in authentication mechanisms for connected terminals. Unauthorized devices on data bus pose serious security risks including the compromise of sensitive data and disruption of communication. Hardware fingerprinting is proposed as a solution for mitigating these risks on MIL-STD-1553. Researches on MIL-STD-1553 hardware fingerprinting have primarily concentrated on a singular machine/deep learning approach, neglecting detailed evaluations of alternative methods' performance metrics. Our study seeks to address this gap by examining a range of machine and deep learning techniques for identifying unauthorized device replacements on the MIL-STD-1553 bus. In addition, optimization of feature extraction and performance of proposed method for signal change over time is considered.


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    Titel :

    Hardware Fingerprinting Using Machine and Deep Learning Methods on MIL-STD-1553


    Beteiligte:
    Evcil, Mustafa (Autor:in) / Tok, Zaliha Yuce (Autor:in) / Babir, Isa Can (Autor:in) / Gokyer, Mehmet Atinc (Autor:in) / Bozkurt, Berat (Autor:in) / Akleylek, Sedat (Autor:in)


    Erscheinungsdatum :

    29.09.2024


    Format / Umfang :

    458242 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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