Risk Assessment of Autonomous Vehicles Using Bayesian Defense Graphs

Behfarnia, Ali; Eslami, Ali

doi:10.1109/vtcfall.2018.8690732

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Security and Communication Networks with corresponding mitigation measures, which can provide a reference for security defense design [27,28].…”

Section: Yesmentioning

confidence: 99%

“…Bayesian network [27,28] Bayesian network is an extension of the Bayesian method. It is one of the most effective theoretical models in the field of uncertain knowledge expression and reasoning, and it is a probabilistic graphical model.…”

Section: Nomentioning

confidence: 99%

“…Besides, the Bayesian defense graph provides a method to calculate the likelihood of threats, which helps to achieve feedback analysis. Behfarnia and Eslami [28] used Bayesian defense graphs to analyze the risk of autonomous vehicles in order to study the effect of countermeasures. ey built a defense graph using the Bayesian network model and parameterized elements of the graph.…”

Section: Feedback-based Approachmentioning

confidence: 99%

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Threat Analysis and Risk Assessment for Connected Vehicles: A Survey

Luo

Jiang

Zhang

et al. 2021

Security and Communication Networks

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With the rapid development of connected vehicles, people can get a better driving experience. However, the interconnection with the external network may bring growing accidents caused by cybersecurity vulnerabilities. As a result, automakers are paying more attention to cybersecurity and spending more cost on developing cybersecurity defense mechanisms. Threat analysis and risk assessment (TARA) is an efficient method to ensure the defense effect and greatly save costs in the early stage of vehicle development. It analyzes the threat of vehicle systems and determines the hierarchical defense and corresponding mitigations according to the potential threat to the system. This paper gives an overview of threat analysis and risk assessment in the automotive field. First, a novel classification of different TARA methods has been proposed. The existing methods have been analyzed and compared. Then, we have found some commonly used tools applied to TARA and compared their performance. After that, a concept named attack-defense mapping is proposed to figure out how to map the already found threats and vulnerabilities of the system to the appropriate mitigations. At last, the future development directions of TARA in the automotive domain have been discussed.

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“…Security and Communication Networks with corresponding mitigation measures, which can provide a reference for security defense design [27,28].…”

Section: Yesmentioning

confidence: 99%

Section: Nomentioning

confidence: 99%

Section: Feedback-based Approachmentioning

confidence: 99%

See 1 more Smart Citation

Threat Analysis and Risk Assessment for Connected Vehicles: A Survey

Luo

Jiang

Zhang

et al. 2021

Security and Communication Networks

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“…On the other hand, a benign node is equipped with a monitoring system to detect abnormal or counterfeit signals. For example, an autonomous vehicle can use a set of anti-spoofing techniques to detect fake global positioning system (GPS) signals [30]. However, benign nodes do not necessarily need to monitor all of their neighbors, due to the cost of monitoring over all short-lived connections.…”

Section: Assumptions and Problem Description A Network Modelmentioning

confidence: 99%

Misbehavior Detection in Ephemeral Networks: A Local Voting Game in Presence of Uncertainty

Behfarnia

Eslami

2019

IEEE Access

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Emerging short-lived (ephemeral) connections between wireless mobile devices have raised concerns over the security of ephemeral networks. An important security challenge in these networks is to identify misbehaving nodes, especially in places where a centrally managed station is absent. To tackle this problem, a local voting-based scheme (game) in which neighboring nodes quickly decide whether to discredit an accused (target) node in mobile networks has been introduced in the literature. However, nodes' beliefs and reactions significantly affect the outcome of target node identification in the collaboration. In this paper, a plain Bayesian game between a benign node and a target node in one stage of a local voting-based scheme is proposed in order to capture uncertainties of nodes for target node identification. In this context, the expected utilities (payoffs) of players in the game are defined according to uncertainties of nodes regarding their monitoring systems, the type of target node and participants, and the outcome of the cooperation. Meanwhile, incentives are offered in payoffs in order to promote cooperation in the network. To discourage nodes from abusing incentives, a variable-benefit approach that rewards each player according to the value of their contribution to the game is introduced. Then, possible equilibrium points between a benign node and a malicious node are derived using a pure-strategy Bayesian Nash equilibrium (BNE) and a mixed-strategy BNE, ensuring that no node is able to improve its payoffs by changing its strategy. Finally, the behavior of malicious and benign nodes is studied via simulations. Specifically, it is shown how the aforementioned uncertainties and the designed incentives impact the strategies of the players and, consequently, the correct target-node identification.

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“…On the other hand, a benign node is equipped with a monitoring system to detect abnormal or counterfeit signals. For example, an autonomous vehicle can use a set of anti-spoofing techniques to detect fake GPS signals [12]. However, benign nodes do not necessarily need to monitor all of their neighbors due to the cost of monitoring over all short-lived connections.…”

Section: A Network Modelmentioning

confidence: 99%

Local Voting Games for Misbehavior Detection in VANETs in Presence of Uncertainty

Behfarnia

Eslami

2019

2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton)

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Cooperation between neighboring vehicles is an effective solution to the problem of malicious node identification in vehicular ad hoc networks (VANETs). However, the outcome is subject to nodes' beliefs and reactions in the collaboration. In this paper, a plain game-theoretic approach that captures the uncertainty of nodes about their monitoring systems, the type of their neighboring nodes, and the outcome of the cooperation is proposed. In particular, one stage of a local voting-based scheme (game) for identifying a target node is developed using a Bayesian game. In this context, incentives are offered in expected utilities of nodes in order to promote cooperation in the network. The proposed model is then analyzed to obtain equilibrium points, ensuring that no node can improve its utility by changing its strategy. Finally, the behavior of malicious and benign nodes is studied by extensive simulation results. Specifically, it is shown how the existing uncertainties and the designed incentives impact the strategies of the players and, consequently, the correct targetnode identification.

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Risk Assessment of Autonomous Vehicles Using Bayesian Defense Graphs

Cited by 9 publications

References 13 publications

Threat Analysis and Risk Assessment for Connected Vehicles: A Survey

Threat Analysis and Risk Assessment for Connected Vehicles: A Survey

Misbehavior Detection in Ephemeral Networks: A Local Voting Game in Presence of Uncertainty

Local Voting Games for Misbehavior Detection in VANETs in Presence of Uncertainty

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