Session Key Distribution Made Practical for CAN and CAN-FD Message Authentication

Xiao, Yang; Shi, Shanghao; Zhang, Ning; Lou, Wenjing; Hou, Yang

doi:10.1145/3427228.3427278

Cited by 11 publications

(5 citation statements)

References 26 publications

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“…These methods enable computations to be carried out beyond the trusted scope of the data sources, while still maintaining the confidentiality of the data [70,17,56]. This growing interest in encryption-based approaches is a response to their potential to offer stronger and more reliable privacy protections in the field of machine learning [66,65,64,57].…”

Section: Cryptographic Techniquesmentioning

confidence: 99%

State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey

Zhang

2024

Preprint

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Section: Cryptographic Techniquesmentioning

confidence: 99%

State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey

Zhang

2024

Preprint

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“…However, Fig 6b clearly shows that the model categories are not entirely delimited by their triangle configuration: for example, models in Kaczmarczyck et al [27], Noor et al [34] and Xiao et al [48] have similar, central triangle placements even though they are members of the deep learning, statistical and respectively simulation categories. Furthermore, even though their topics are also different, namely automated malware family identification, illustrating a mechanism for key distribution on automotive networks and analysing the forensic validity of approximated audit logs, they all obtain a more balanced configuration by introducing qualitative reasoning about their inner workings.…”

Section: Simulation Modelsmentioning

confidence: 99%

Meta-modelling for Ecosystems Security

Caulfield

Ilau

Pym

2022

Simulation Tools and Techniques

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As the world has evolved to become ever more dependent on complex ecosystems of large, interacting systems, it has become ever more important to be able to reason rigorously about the design, construction, and behaviour not only of individual systems -which may include aspects related to all of people, process, and technology -but also of their assembly into ecosystems. In such situations, it is inevitable that no one type of model -such as mathematical models of dynamical systems, logical models of languages, or discrete event simulation models -will be sufficient to describe all of the aspects of ecosystems about which rigorous reasoning is required. We propose here a metatheoretical framework, the 'triangle framework', within which different types of models may be categorized and their interactions, especially during the construction of models, can be understood. Its explicit goals are to facilitate a better understanding of the nature of models and to provide a more inclusive language for the description of heterogeneous models. Specifically, we identify three qualities of models, each derived from modelling goals -conceptuality, mathematicality, and executability -and explain how models will, typically, have all of these qualities to varying extents. We also show how the framework supports an analysis of how models can be co-designed by their various stakeholders within an identified translation zone within the process of model construction. We explore our ideas in the concrete setting of models encountered in a range of surveyed security papers, drawn from a diverse collection of security conferences. Although descriptive in nature, we envision this framework as a necessary first step in the development of a methodology for heterogeneous model design and construction, diverse enough to characterize the myriad of model types used in the field of information security while at the same time addressing validation concerns that can reduce their usability in the area of security decision-making.

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“…Weaknesses in communication protocols [18] can be exploited to intercept or alter data flow between RL agents and their environments, compromising the decision-making process. The dependence on wireless communications exposes RL systems to spectrum-based attacks [30,43,41,42], such as jamming or spoofing [29], potentially crippling their functionality. Moreover, the involvement of untrusted or compromised service providers magnifies the threat landscape, facilitating malicious activities like data tampering or unauthorized data access [45,7,6].…”

Section: Adversarial Examples Attack and Defensementioning

confidence: 99%

Cyberspace attack and defense game based on reward randomization reinforcement learning

Zhang

Pan

et al. 2022

Array

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Session Key Distribution Made Practical for CAN and CAN-FD Message Authentication

Cited by 11 publications

References 26 publications

State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey

State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey

Meta-modelling for Ecosystems Security

Cyberspace attack and defense game based on reward randomization reinforcement learning

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