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Keywords = extended finite state machine (EFSM)

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38 pages, 1825 KiB  
Article
Graph-Based Automation of Threat Analysis and Risk Assessment for Automotive Security
by Mera Nizam-Edden Saulaiman, Miklos Kozlovszky and Akos Csilling
Information 2025, 16(6), 449; https://doi.org/10.3390/info16060449 - 27 May 2025
Viewed by 1605
Abstract
The proliferation of cyber–physical systems in modern vehicles, characterized by densely interconnected Electronic Control Units (ECUs) and heterogeneous communication networks, has significantly expanded the automotive attack surface. Traditional Threat Analysis and Risk Assessment (TARA) methodologies remain predominantly manual processes that exhibit limitations in [...] Read more.
The proliferation of cyber–physical systems in modern vehicles, characterized by densely interconnected Electronic Control Units (ECUs) and heterogeneous communication networks, has significantly expanded the automotive attack surface. Traditional Threat Analysis and Risk Assessment (TARA) methodologies remain predominantly manual processes that exhibit limitations in scalability, and comprehensive threat identification. This research addresses these limitations by developing a formalized framework for automating attack path analysis within the automotive architecture. While attack graph methodologies have demonstrated efficacy in conventional information technology domains, their application within automotive cybersecurity contexts presents unique challenges stemming from domain-specific architectural constraints. We propose a novel Graph-based Attack Path Prioritization (GAPP) methodology that integrates Extended Finite State Machine (EFSM) modeling. Our implementation employs the Neo4j property graph database architecture to establish the mappings between architectural components, security states, and exploitation vectors. This research contributes a systematic approach to automotive security assessment, enhancing vulnerability identification capabilities while reducing analytical complexity. Full article
(This article belongs to the Special Issue Emerging Information Technologies in the Field of Cyber Defense)
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23 pages, 3761 KiB  
Article
Real-Time Fault Diagnosis and Fault-Tolerant Control Strategy for Hall Sensors in Permanent Magnet Brushless DC Motor Drives
by Xi Zhang, Yiyun Zhao, Hui Lin, Saleem Riaz and Hassan Elahi
Electronics 2021, 10(11), 1268; https://doi.org/10.3390/electronics10111268 - 25 May 2021
Cited by 17 | Viewed by 4604
Abstract
The Hall sensor is the most commonly used position sensor of the permanent magnet brushless direct current (PMBLDC) motor. Its failure may lead to a decrease in system reliability. Hence, this article proposes a novel methodology for the Hall sensors fault diagnosis and [...] Read more.
The Hall sensor is the most commonly used position sensor of the permanent magnet brushless direct current (PMBLDC) motor. Its failure may lead to a decrease in system reliability. Hence, this article proposes a novel methodology for the Hall sensors fault diagnosis and fault-tolerant control in PMBLDC motor drives. Initially, the Hall sensor faults are analyzed and classified into three fault types. Taking the Hall signal as the system state and the conducted MOSFETs as the system event, the extended finite state machine (EFSM) of the motor in operation is established. Meanwhile, a motor speed observer based on the super twisting algorithm (STA) is designed to obtain the speed signal of the proposed strategy. On this basis, a real-time Hall sensor fault diagnosis strategy is established by combining the EFSM and the STA speed observer. Moreover, this article proposes a Hall signal reconstruction strategy, which can generate compensated Hall signal to realize fault-tolerant control under single or double Hall sensor faults. Finally, theoretical analysis and experimental results validate the superior effectiveness of the proposed real-time fault diagnosis and fault-tolerant control strategy. Full article
(This article belongs to the Special Issue Advancement of Fault Detection/Diagnosis and Fault-Tolerant Control with Applications)
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