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Connected and Automated Vehicles (CAVs) cybersecurity is an inherently complex, multi-dimensional issue that goes beyond isolated hardware or software vulnerabilities, extending to human threats, network vulnerabilities, and broader system-level risks. Currently, no formal, comprehensive tool exists that integrates these diverse dimensions into a u...
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... Khan et al. Transport Policy 162 (2025) 47-64 improvement in PHD only, illustrated in Fig. 10. PHD is augmented through a reinforcing mechanism that runs through CAV Users and OEMs Education Effectiveness. PHD improvements reduce effective cyber assaults, allowing for a rise in CAV Adopters. CAVs penetration occurs via Adoption and Induction, leading to a rise in CAV Adopters. The change in PHA is amplified because of a ...
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... insights into improving the security of ITS. Therefore, the safety of CAV-based ITS may be assessed using data models and applications ( Lian et al., 2020). On the flip side, as the quantity of CAVs on the road increases, hackers can explore novel models for generating profit. The growing number of access points in CAVs raises the PHA (shown in Fig. 10), increasing cyberattacks. Increased cyberattacks will undoubtedly derail CAV adoption, necessitating an adaptive, holistic approach to avert this ...
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... proper information dissemination management, test-ride events and transparency on safety solutions would all have an impact on CAV's overall social acceptability, improving cybersecurity. The scenario 'CAVs penetration with public behaviour analysis' has a noticeable decrease in PHA (illustrated in Fig. 10). Characterising individuals vulnerable to cybersecurity failures, managing the impact of reduced driver-driving abilities, and understanding the motives and attributes of CAV cyberattacks (psychological profile) ( Kennedy et al., 2019;Wilson and Hash, 2003;Christiansen and Piekarz, 2018) have the potential to improve CAV ...
Citations
... While these technologies offer robust solutions to security concerns, they also introduce challenges related to implementation costs, system complexity, and user adoption ( Table 3). Overcoming these barriers will require collaborative efforts between automotive manufacturers, cybersecurity experts, and regulatory bodies to create standardized frameworks and cost-effective solutions [58][59][60][61][62][63][64]. ...
This study investigates the integration of smart card readers into vehicle ignition systems as a multifaceted solution to enhance security, regulatory compliance, and road safety. By implementing real-time driver verification, encryption protocols (AES-256, RSA), and multifactor authentication, the system significantly reduces unauthorized vehicle use and improves accident prevention. A critical advancement of this research is the incorporation of automated drug and impairment detection to prevent driving under the influence of substances, including illicit drugs and prescription medications. Risk models estimate that drug-related accidents could be reduced by 7.65% through the integration of these technologies into vehicle ignition systems, assuming high compliance rates. The study evaluates drug applications leveraging the same sensor-based monitoring technologies as used for impairment detection. These systems can facilitate the real-time tracking of medication intake and physiological responses, offering new possibilities for safety applications in medical transportation and assisted driving technologies. High-performance polymers such as polyetheretherketone (PEEK) enhance the durability and thermal stability of smart card readers, while blockchain-based verification strengthens data security and regulatory compliance. Despite challenges related to cost (USD 100–300 per unit) and adherence to ISO standards, these innovations position smart card-based ignition systems as a comprehensive, technology-driven approach to vehicle security, impairment prevention, and medical monitoring.
... Security and Privacy Metrics[7] ...
... Scalability and Future Technology Metrics Scalability Considerations[7,8] The automotive industry has embraced cloud-native architectures utilizing microservices, with each vehicle platform supporting an average of 1,500 distinct services.Edge computing nodes process 85% of non-critical operations locally, reducing cloud data transmission by 75% while maintaining response times under 50ms for critical functions. The Digital Transformation in the Automobile Industry: A Technical Analysis of Customer Success Enhancement https://iaeme.com/Home/journal/IJRCAIT ...
... Security and Privacy Metrics[7] ...
... Scalability and Future Technology Metrics Scalability Considerations[7,8] The automotive industry has embraced cloud-native architectures utilizing microservices, with each vehicle platform supporting an average of 1,500 distinct services.Edge computing nodes process 85% of non-critical operations locally, reducing cloud data transmission by 75% while maintaining response times under 50ms for critical functions. The Digital Transformation in the Automobile Industry: A Technical Analysis of Customer Success Enhancement https://iaeme.com/Home/journal/IJRCAIT ...
This article presents a comprehensive analysis of digital transformation in the automotive industry, focusing on customer success enhancement through technological innovation. The article examines the implementation of advanced technologies across the automotive value chain, from manufacturing to post-purchase services. Through analysis of industry data, the article demonstrates that digital transformation initiatives have led to significant improvements in customer satisfaction and operational efficiency. The article investigates four key areas: IoT integration and vehicle connectivity, data analytics infrastructure, digital customer journey implementation, and security considerations. The article reveals that connected vehicle platforms process the average data per driving hour, while AI-powered customer support systems accuracy in natural language processing. The article also addresses critical security challenges and future technological directions, highlighting the industry's move toward quantum-resistant security protocols and cloud-native architectures. This article provides valuable insights into the technological advancement of the automotive sector and its impact on customer experience enhancement.
... Security and Privacy Metrics[7] ...
... Scalability and Future Technology Metrics Scalability Considerations[7,8] The automotive industry has embraced cloud-native architectures utilizing microservices, with each vehicle platform supporting an average of 1,500 distinct services.Edge computing nodes process 85% of non-critical operations locally, reducing cloud data transmission by 75% while maintaining response times under 50ms for critical functions. The Digital Transformation in the Automobile Industry: A Technical Analysis of Customer Success Enhancement https://iaeme.com/Home/journal/IJRCAIT ...
This article presents a comprehensive analysis of digital transformation in the automotive industry, focusing on customer success enhancement through technological innovation. The article examines the implementation of advanced technologies across the automotive value chain, from manufacturing to post-purchase services. Through analysis of industry data, the article demonstrates that digital transformation initiatives have led to significant improvements in customer satisfaction and operational efficiency. The article investigates four key areas: IoT integration and vehicle connectivity, data analytics infrastructure, digital customer journey implementation, and security considerations. The article reveals that connected vehicle platforms process the average data per driving hour, while AI-powered customer support systems accuracy in natural language processing. The article also addresses critical security challenges and future technological directions, highlighting the industry's move toward quantum-resistant security protocols and cloud-native architectures. This article provides valuable insights into the technological advancement of the automotive sector and its impact on customer experience enhancement.
... Performance analysis demonstrates encryption overhead averaging 0.45ms per transaction while maintaining data throughput of 1.2 GB/second across vehicle subsystems. The implementation achieves NIST FIPS 140-3 Level 3 compliance while ensuring all V2X communications maintain confidentiality through quantumresistant algorithms [11]. Data integrity assurance is achieved through a distributed ledger system implementing a Delegated Proof of Stake (DPoS) consensus mechanism optimized for automotive applications. ...
... Each vehicle maintains a lightweight blockchain client requiring 180MB of storage, with intelligent pruning maintaining a 14-day verification window. Field testing demonstrates 99.95% successful verification rates under varying network conditions, with false positive rates below 0.005% [11]. The secure update framework employs a hardware-backed root of trust utilizing automotivegrade HSMs with ECC P-384 signatures. ...
... The neural network backend, trained on 5.5 million documented attack patterns, achieves 95.4% detection accuracy with a false positive rate of 0.08%. Response automation initiates defensive measures within 120ms of threat confirmation, implementing a graduated response protocol based on threat classification [11]. Network protection incorporates micro-segmentation with dedicated virtual networks for critical subsystems. ...
The rapid evolution of autonomous vehicle networks demands advanced real-time data processing capabilities to ensure safety, efficiency, and seamless operation. This article presents a novel approach to real-time processing within these networks, leveraging scalable, low-latency cloud architectures and edge computing to support instantaneous data analysis and decision-making. By distributing computational tasks across edge nodes and centralized cloud platforms, our solution minimizes latency, enhances data accuracy, and ensures robust vehicle-to-vehicle and vehicle-to-infrastructure communication. The system incorporates a predictive AI model that dynamically adapts to traffic conditions, sensor inputs, and external environmental factors, enabling autonomous vehicles to make highly reliable split-second decisions. This innovative approach addresses existing limitations in network latency, data synchronization, and computational constraints, offering a transformative leap forward in autonomous vehicle network performance. Experimental results demonstrate significant improvements in processing efficiency, system reliability, and predictive accuracy across diverse operational conditions, establishing new benchmarks for autonomous vehicle systems.
