“…Methodologically, this paper employs STRIDE-DREAD modeling and a comprehensive literature review to analyze security threats in vehicular systems, a technique not commonly employed in studies in [ 60 , 61 ]. This paper’s rigorous evaluation of existing literature, assessing both the strengths and limitations of prior research, aligns with but extends beyond the analyses in works like [ 63 , 64 ].…”
Section: Discussionmentioning
confidence: 99%
“… Figure A2 Detailed defense taxonomy for CCAV attack mechanisms. Figure A3 Number of identified threats from the taxonomy against other papers in the literature [ 3 , 23 , 25 , 27 , 29 , 34 , 39 , 40 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ], adapted from [ 8 ]. …”
As threat vectors and adversarial capabilities evolve, Cloud-Assisted Connected and Autonomous Vehicles (CCAVs) are becoming more vulnerable to cyberattacks. Several established threat analysis and risk assessment (TARA) methodologies are publicly available to address the evolving threat landscape. However, these methodologies inadequately capture the threat data of CCAVs, resulting in poorly defined threat boundaries or the reduced efficacy of the TARA. This is due to multiple factors, including complex hardware–software interactions, rapid technological advancements, outdated security frameworks, heterogeneous standards and protocols, and human errors in CCAV systems. To address these factors, this study begins by systematically evaluating TARA methods and applying the Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privileges (STRIDE) threat model and Damage, Reproducibility, Exploitability, Affected Users, and Discoverability (DREAD) risk assessment to target system architectures. This study identifies vulnerabilities, quantifies risks, and methodically examines defined data processing components. In addition, this study offers an attack tree to delineate attack vectors and provides a novel defense taxonomy against identified risks. This article demonstrates the efficacy of the TARA in systematically capturing compromised security requirements, threats, limits, and associated risks with greater precision. By doing so, we further discuss the challenges in protecting hardware–software assets against multi-staged attacks due to emerging vulnerabilities. As a result, this research informs advanced threat analyses and risk management strategies for enhanced security engineering of cyberphysical CCAV systems.
“…Methodologically, this paper employs STRIDE-DREAD modeling and a comprehensive literature review to analyze security threats in vehicular systems, a technique not commonly employed in studies in [ 60 , 61 ]. This paper’s rigorous evaluation of existing literature, assessing both the strengths and limitations of prior research, aligns with but extends beyond the analyses in works like [ 63 , 64 ].…”
Section: Discussionmentioning
confidence: 99%
“… Figure A2 Detailed defense taxonomy for CCAV attack mechanisms. Figure A3 Number of identified threats from the taxonomy against other papers in the literature [ 3 , 23 , 25 , 27 , 29 , 34 , 39 , 40 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ], adapted from [ 8 ]. …”
As threat vectors and adversarial capabilities evolve, Cloud-Assisted Connected and Autonomous Vehicles (CCAVs) are becoming more vulnerable to cyberattacks. Several established threat analysis and risk assessment (TARA) methodologies are publicly available to address the evolving threat landscape. However, these methodologies inadequately capture the threat data of CCAVs, resulting in poorly defined threat boundaries or the reduced efficacy of the TARA. This is due to multiple factors, including complex hardware–software interactions, rapid technological advancements, outdated security frameworks, heterogeneous standards and protocols, and human errors in CCAV systems. To address these factors, this study begins by systematically evaluating TARA methods and applying the Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privileges (STRIDE) threat model and Damage, Reproducibility, Exploitability, Affected Users, and Discoverability (DREAD) risk assessment to target system architectures. This study identifies vulnerabilities, quantifies risks, and methodically examines defined data processing components. In addition, this study offers an attack tree to delineate attack vectors and provides a novel defense taxonomy against identified risks. This article demonstrates the efficacy of the TARA in systematically capturing compromised security requirements, threats, limits, and associated risks with greater precision. By doing so, we further discuss the challenges in protecting hardware–software assets against multi-staged attacks due to emerging vulnerabilities. As a result, this research informs advanced threat analyses and risk management strategies for enhanced security engineering of cyberphysical CCAV systems.
“…As shown in Table 1, the threats to CCAVs were classified in [28,36] and V2V/V2I communications were classified in [15][16][17][18][19][20][21][22][23][24][25][26][27][29][30][31][32][33][35][36][37]. Its impacts are highlighted in [31,32,34,36,37]; however, none have mapped and illustrated the threats with the trust domains. Common attack vectors were only discussed in [31,32,37], although an in-depth analysis of the vectors with taxonomy is not discussed.…”
Cloud-Assisted Connected and Autonomous Vehicles (CCAV) are set to revolutionise road safety, providing substantial societal and economic advantages. However, with the evolution of CCAV technology, security and privacy threats have increased. Although several studies have been published around the threat and risk estimation aspects of CCAV, limited research exists on the security implications and emerging threat landscapes in the CCAV platooning application. We conducted an extensive review and categorisation of real-world security incidents and created an account of 132 threats from scholarly sources and 64 threats from recorded events in practice. Furthermore, we defined thirty-one (31) trust domains and outlined eight (8) unique attack vectors to supplement existing research efforts for the systematic security analysis of such cyberinfrastructures. Using these findings, we create a detailed attack taxonomy to communicate threat-related information in CCAV and platooning applications and highlight emerging challenges and ways to safeguard the broader CCAV systems. This work acts as a roadmap to existing researchers and practitioners advocating for a ‘security and privacy by design’ framework for a dynamically evolving CCAV threat landscape.
“…Potential attackers could exploit vulnerabilities within the V2X network through various connection points, including links within the controller network connecting the CAN bus with ECUs, interconnections among ECUs, connections from ECUs to actuators, and even targeting internal sensors and actuators themselves, highlighting the vulnerabilities in in-vehicle communication. In contemporary vehicles, the proliferation of ECUs, ranging from 70 to 100 [29], in contrast to only two ECUs in the 1980s [30], has significantly escalated the attack surface. Furthermore, CAVs are exposed to heightened risk due to their diverse onboard computer connections, encompassing both wireless interfaces like WiFi for external devices and physical connections like Ethernet and USB, extending to sensors, dashboards, and externally introduced devices.…”
This systematic review focuses on anomaly detection for connected and autonomous vehicles. The initial database search identified 2160 articles, of which 203 were included in this review after rigorous screening and assessment. This study revealed that the most commonly used Artificial Intelligence (AI) algorithms employed in anomaly detection are neural networks like LSTM, CNN, and autoencoders, alongside one-class SVM. Most anomaly-based models were trained using real-world operational vehicle data, although anomalies, such as attacks and faults, were often injected artificially into the datasets. These models were evaluated mostly using five key evaluation metrics: recall, accuracy, precision, F1-score, and false positive rate. The most frequently used selection of evaluation metrics used for anomaly detection models were accuracy, precision, recall, and F1-score.This systematic review presents several recommendations. First, there is a need to incorporate multiple evaluation metrics to provide a comprehensive assessment of the anomaly detection models. Second, only a small proportion of the studies have made their models open source, indicating a need to share models publicly to facilitate collaboration within the research community, and to validate and compare findings effectively. Third, there is a need for benchmarking datasets with predefined anomalies or cyberattacks to test and improve the effectiveness of the proposed anomaly-based detection models. Furthermore, there is a need for future research to investigate the deployment of anomaly detection to a vehicle to assess its performance on the road. There is a notable lack of research done on intrusion detection systems using different protocols to CAN, such as Ethernet and FlexRay.
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