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  • Open Access

    ARTICLE

    Comprehensive Black-Box Fuzzing of Electric Vehicle Charging Firmware via a Vehicle to Grid Network Protocol Based on State Machine Path

    Yu-Bin Kim, Dong-Hyuk Shin, Ieck-Chae Euom*

    CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2217-2243, 2025, DOI:10.32604/cmc.2025.063289 - 03 July 2025

    Abstract The global surge in electric vehicle (EV) adoption is proportionally expanding the EV charging station (EVCS) infrastructure, thereby increasing the attack surface and potential impact of security breaches within this critical ecosystem. While ISO 15118 standardizes EV-EVCS communication, its underspecified security guidelines and the variability in manufacturers’ implementations frequently result in vulnerabilities that can disrupt charging services, compromise user data, or affect power grid stability. This research introduces a systematic black-box fuzzing methodology, accompanied by an open-source tool, to proactively identify and mitigate such security flaws in EVCS firmware operating under ISO 15118. The proposed… More >

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  • Open Access

    ARTICLE

    KubeFuzzer: Automating RESTful API Vulnerability Detection in Kubernetes

    Tao Zheng1, Rui Tang1,2,3, Xingshu Chen1,2,3,*, Changxiang Shen1

    CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1595-1612, 2024, DOI:10.32604/cmc.2024.055180 - 15 October 2024

    Abstract RESTful API fuzzing is a promising method for automated vulnerability detection in Kubernetes platforms. Existing tools struggle with generating lengthy, high-semantic request sequences that can pass Kubernetes API gateway checks. To address this, we propose KubeFuzzer, a black-box fuzzing tool designed for Kubernetes RESTful APIs. KubeFuzzer utilizes Natural Language Processing (NLP) to extract and integrate semantic information from API specifications and response messages, guiding the generation of more effective request sequences. Our evaluation of KubeFuzzer on various Kubernetes clusters shows that it improves code coverage by 7.86% to 36.34%, increases the successful response rate by More >

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