Cybersecurity protection on in‐vehicle networks for distributed automotive cyber‐physical systems: State‐of‐the‐art and future challenges

Xie, Yong; Zhou, Yongzhi; Xu, Jianwei; Zhou, Jian; Chen, Xiaobai; Xiao, Fu

doi:10.1002/spe.2965

Cited by 24 publications

(13 citation statements)

References 66 publications

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“…To enable the sustainable development of CPS, innovative technologies for improving system dependability are necessary. The article 1 reviews and compares the state‐of‐the‐art approaches designed for availability protection, integrity protection, and confidentiality protection of in‐vehicle CPS. In this article, the authors also summarized the future challenges for cybersecurity protection of in‐vehicle CPS and provided several possible solutions for these challenges.…”

Section: Summary Of the Contributionsmentioning

confidence: 99%

Software and hardware co‐design for sustainable cyber‐physical systems

et al. 2021

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Section: Summary Of the Contributionsmentioning

confidence: 99%

Software and hardware co‐design for sustainable cyber‐physical systems

et al. 2021

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“…Even now in vehicle network, due to this malicious link, cybersecurity defense will face a number of challenges in the future. 19 . In the modern‐day, phishing is one of the biggest concerns for the internet user.…”

Section: Related Work and Research Gapmentioning

confidence: 99%

“…Each of these "trust tokens" makes the email appear more legit and this, in turn, drives open and clicks rates allowing the hackers to gain full access to our information. Even now in vehicle network, due to this malicious link, cybersecurity defense will face a number of challenges in the future.. 19 In the modern-day, phishing is one of the biggest concerns for the internet user.…”

Section: Background Study On Urlmentioning

confidence: 99%

Intelligent antiphishing framework to detect phishing scam: A hybrid classification approach

Marimuthu¹,

Gopalasamy²,

Ben‐Othman

2021

Softw Pract Exp

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In the inevitably hi‐tech universe of cybercrimes, one of the major still prevailing methods is the usage of the malicious URLs and creating a Phishing link to obtain user credentials from the people. This method is highly subtle and has more effect on people's lives as well as corporate loss. To identify the malicious URLs, the security community has listed blacklists and benign links online. Still, as the technology is being developed day by day, the attackers try to create new phishing URLs using new social engineering methods that could be easily forged into the user's account. To improve the generality of malicious URL detectors, machine‐learning techniques have been explored with increasing attention in recent years. This article addresses the detection of malicious URLs combining the intelligence of both the heuristic‐based method and the machine learning process. It has been found that there are many possibilities for detecting zero‐day attacks and spear‐phishing attacks when incorporating both lexical features and machine learning methods. Out of the six‐batch learning process analyzed, we implement a decision tree algorithm in our framework with 99.47% accuracy during evaluation. The true positive values gained in our proposed hybrid framework is 99.2% and indicate <1% of the false‐positive values. The implementation shows a precision level higher than the previous model developed and by other antiphishing techniques. A high detection rate on zero‐day and spear‐phishing attacks and overall results reveal that our system outclasses the current approach to detecting phishing scams.

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“…Under the background of automobile intelligence and electric vehicles, autonomous vehicles technology has developed rapidly. Autonomous vehicles are considered to be one of the best solutions for urban traffic in the future 1 . The earliest autonomous vehicles technology mainly relied on a variety of sensors of local vehicles.…”

Section: Introductionmentioning

confidence: 99%

Online multitarget tracking system for autonomous vehicles using discriminative dictionary learning with embedded auto‐encoder algorithm

Jiang

2022

Softw Pract Exp

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With the advancements in 5G network and mobile edge computing technology, autonomous vehicle technology has gained new development opportunities. Multitarget tracking becomes the research hotspots in autonomous vehicles. Since many factors such as motion blur, partial occlusion, and illumination changes affect the performance of target tracking, the problem of target tracking is still an open topic. In this article, inspired by the strong discriminative ability of dictionary learning, the discriminative dictionary learning with embedded auto‐encoder (DDLEA) algorithm is developed for the multitarget tracking system. The DDLEA algorithm integrates the auto‐encoder into the dictionary learning framework and learns sparse representations while preserving the local structure and discriminative information of data. The learned dictionary model has the strong recognition ability. Further, a multitarget tracking system is developed based on the proposed DDLEA algorithm and the hierarchical data association scheme. Based on the target confidence in the STKSVD model, the hierarchical data association method first uses the Hungarian algorithm to complete the preliminary matching of high confidence targets, and then further tracks the low confidence targets to improve the tracking ability. Experiments are carried out the public MOT 2015 dataset. Compared with several popular multitarget tracking algorithms, the tracking performance of our system is satisfactory.

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Cybersecurity protection on in‐vehicle networks for distributed automotive cyber‐physical systems: State‐of‐the‐art and future challenges

Cited by 24 publications

References 66 publications

Software and hardware co‐design for sustainable cyber‐physical systems

Software and hardware co‐design for sustainable cyber‐physical systems

Intelligent antiphishing framework to detect phishing scam: A hybrid classification approach

Online multitarget tracking system for autonomous vehicles using discriminative dictionary learning with embedded auto‐encoder algorithm

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