Efficient Attack Forest Construction for Automotive On-board Networks

Salfer, Martin; Schweppe, Hendrik; Eckert, Claudia

doi:10.1007/978-3-319-13257-0_27

Cited by 11 publications

(7 citation statements)

References 12 publications

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“…e algorithm can automatically find the optimal attack path between the attacker and the asset with the aid of the system model. Reference [35] also proves that even in the worst case, this method can complete the threat analysis and security assessment of a large system within a few minutes. is is very beneficial to OEMs, who often need to perform large-scale threat analysis on vehicle systems.…”

Section: Tree-based Methodsmentioning

confidence: 86%

“…It is inevitable that attack paths will be lost and the possibility of vehicle systems being attacked will increase, which is unacceptable to the OEMs. In response to this shortcoming of attack tree analysis, Salfer et al [35] proposed a method for automatically constructing attack forests for automotive networks for software attacks. e algorithm can automatically find the optimal attack path between the attacker and the asset with the aid of the system model.…”

Section: Tree-based Methodsmentioning

confidence: 99%

“…Tree-based ATA (attack tree analysis) [34,35] Attack tree analysis is a formal and clear method used to describe the security threats faced by the system and the various attacks that the system may be subjected to. It is able to describe the complex attack process in the form of a tree, but this method requires more details of the system design.…”

Section: Yesmentioning

confidence: 99%

See 2 more Smart Citations

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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Section: Tree-based Methodsmentioning

confidence: 86%

Section: Tree-based Methodsmentioning

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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“…As regards this, [14] mentioned all the possible attack categories and cases that can be happened for driverless vehicles. To be more specified, intrusions may occur to automotive control system, such as ECU [25], driving system components, such as sensors [1,27], and during the vehicle-to-everything communications [4,9]. Besides, outstanding defense approaches in the last decade were also discussed in the paper [11].…”

Section: Related Workmentioning

confidence: 99%

Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation

Nguyen¹,

Nguyen²,

Leppänen³

et al. 2022

Preprint

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Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and environmental and contextual data becomes imperative. As a part of such Intelligent Traffic Systems, cooperation between different stakeholders needs to be facilitated rapidly, reliably, and securely. The Internet of Things provides the fabric to connect these stakeholders who share their data, refined information, and provided services with each other. However, these cloud-based systems struggle to meet the real-time requirements for smart traffic due to long distances across networks. Here, edge computing systems bring the data and services into the close proximity of fast-moving vehicles, reducing information delivery latencies and improving privacy as sensitive data is processed locally. To solve the issues of trust and latency in data sharing between these stakeholders, we propose a decentralized framework that enables smart contracts between traffic data producers and consumers based on blockchain. Autonomous vehicles connect to a local edge server, share their data, or use services based on agreements, for which the cooperating edge servers across the system provide a platform. We set up proof-of-concept experiments with Hyperledger Fabric and virtual cars to analyze the system throughput with secure unicast and multicast data transmissions. Our results show that multicast transmissions in such a scenario boost the throughput up to 2.5 times where the data packets of different sizes can be transmitted in less than one second.

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“…The foundations of formal descriptions of attacks were laid by [15] and in particular their process algebraic nature was recognised in [28]. Automotive specific attack trees have also been discussed in literature where [24] looked at attack tree generation and gave formal descriptions of the trees. This is orthogonal to our research (in translating low level attack trees) as in our case the trees have already been pre-built based on reconnaissance of a black box system, rather than the automatic generation of an attack tree from a fully specified systemunder-test (SUT).…”

Section: Attack Treesmentioning

confidence: 99%

Formalising Systematic Security Evaluations Using Attack Trees for Automotive Applications

Cheah

Nguyen

Bryans

et al. 2018

Lecture Notes in Computer Science

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Vehicles are insecure. To protect such systems, we must begin by identifying any weaknesses. One approach is to apply a systematic security evaluation to the system under test. In this paper we present a method for systematically generating tests based on attack trees. We formalise the attack trees as provably-equivalent process-algebraic processes, then automatically generate tests from the process-algebraic representation. Attack trees may include manual input, (and thus so will some test cases) but scriptable test cases are automatically executed. Our approach is inspired by model based testing, but allows for the fact that we do not have a specification of the system under test. We demonstrate this methodology on a case study and find that this is a viable method for automation of systematic security evaluations.

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Efficient Attack Forest Construction for Automotive On-board Networks

Cited by 11 publications

References 12 publications

Threat Analysis and Risk Assessment for Connected Vehicles: A Survey

Threat Analysis and Risk Assessment for Connected Vehicles: A Survey

Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation

Formalising Systematic Security Evaluations Using Attack Trees for Automotive Applications

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