Anomaly Detection in Vehicle-to-Infrastructure Communications

Russo, Michele; Labonne, Maxime; Olivereau, Alexis; Rmayti, Mohammad

doi:10.1109/vtcspring.2018.8417863

Cited by 7 publications

(4 citation statements)

References 16 publications

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“…Enhance the running performance to a real-time level. We observe that many studies [11,84,96] in the smart vehicle domain discussed the running performance of DLAD methods. This is because the response time is critical to avoid devastating accidents in smart cars.…”

Section: Improving Deep Anomaly Detection Methodsmentioning

confidence: 89%

“…Characteristics of DLAD methods in ITSs. Most studies in ITS aim to detect attacks on the CAN bus [47,84,94,116], which is responsible for the communication between devices (e.g., airbags) and Electronic Control Units (ECUs) [19]. Khanapuri 6.…”

Section: Dlad Methods In Itssmentioning

confidence: 99%

“…Through investigating anomalies that can possibly happen, several independent abnormal cases can be injected into the normal data series. For instance, Taylor et al [94] and Russo et al [84] injected several attack cases into the sequence of CAN bus packets. (2) Statistical anomaly.…”

Section: Implementation and Evaluation Metricsmentioning

confidence: 99%

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Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities

Luo,

Xiao,

Cheng

et al. 2020

Preprint

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Anomaly detection is crucial to ensure the security of cyber-physical systems (CPS). However, due to the increasing complexity of CPSs and more sophisticated attacks, conventional anomaly detection methods cannot be directly applied to thwart such issues, which also need domain-specific knowledge and handle the growing volume of data. Deep learning-based anomaly detection (DLAD) methods have been proposed to achieve unsupervised detection in the era of CPS big data. In this paper, we review state-of-the-art DLAD methods in CPSs. We propose a taxonomy in terms of the type of anomalies, strategies, implementation and evaluation metrics to understand the essential properties of current methods. Further, we utilize this taxonomy to identify and highlight new characteristics and designs in each CPS domain. We summarise a list of publicly available datasets for training and evaluation. We also discuss our findings, the limitations of existing studies, and possible directions to improve DLAD methods.CCS Concepts: • Security and privacy → Intrusion/anomaly detection and malware mitigation; • Computer systems organization → Embedded and cyber-physical systems.

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Section: Improving Deep Anomaly Detection Methodsmentioning

confidence: 89%

Section: Dlad Methods In Itssmentioning

confidence: 99%

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Deep Learning-Based Anomaly Detection in Cyber-Physical Systems: Progress and Opportunities

Luo,

Xiao,

Cheng

et al. 2020

Preprint

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“…The main challenge of designing a distributed attack detection method is monitoring cyber-physical systems without adequate information. Most cyber-physical systems lack various cyber security mechanisms, such as message authentication, which results in numerous challenges for detecting data injection attacks [20]. The absence of worldwide encryption, mainly on systems engaging in dated technologies, makes it exciting to secure in contradiction of eavesdropping attacks.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems

Gaba,

Budhiraja,

Kumar

et al. 2024

IEEE Access

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In this current era, cyber-physical systems (CPSs) have gained concentrated consideration in various fields because of their emergent applications. Though the robust dependence on communication networks creates cyber-physical systems susceptible to deliberated cyber related attacks and detecting these cyber-attacks are the most challenging task. There is the interaction among the components of the cyber and physical worlds, so CPS security needs a distinct approach from past security concerns. Deep learning (DL) distributes better performance than machine learning (ML) due to its layered architecture and the efficient algorithm for extracting prominent information from training data. So, the deep learning models are taken into consideration quickly for detecting cyber-attacks in cyber physical systems. As numerous attack detection methods have been proposed by various authors for enforcing CPS security, this paper reviews and analyzes multiple ways of attack detection presented for CPS using deep learning. We will be putting the excellent potential for detecting cyber-attacks for CPS concerning deep learning modules. The admirable performance is attained partly as highly quality datasets are eagerly obtainable for the use of the public. Moreover, various challenges and research inclinations are also discussed in impending research.

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