Abstract interpretation

Cousot, Patrick; Cousot, Radhia

doi:10.1145/2603088.2603165

Cited by 38 publications

(5 citation statements)

References 192 publications

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“…Abstract interpretation is a rigorous technique that offers a structured approach to estimating the behavior of a system through the abstraction of its concrete components [98][99][100]. It provides a framework for analyzing programs or systems by approximating their behaviors using abstract representations [101][102][103]. These abstract representations capture essential properties of the system while disregarding irrelevant details, enabling scalable analysis and aiding in the identification of security vulnerabilities [104][105][106].…”

Section: Abstract Interpretationmentioning

confidence: 99%

Formal Methods and Validation Techniques for Ensuring Automotive Systems Security

Krichen

2023

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The increasing complexity and connectivity of automotive systems have raised concerns about their vulnerability to security breaches. As a result, the integration of formal methods and validation techniques has become crucial in ensuring the security of automotive systems. This survey research paper aims to provide a comprehensive overview of the current state-of-the-art formal methods and validation techniques employed in the automotive industry for system security. The paper begins by discussing the challenges associated with automotive system security and the potential consequences of security breaches. Then, it explores various formal methods, such as model checking, theorem proving, and abstract interpretation, which have been widely used to analyze and verify the security properties of automotive systems. Additionally, the survey highlights the validation techniques employed to ensure the effectiveness of security measures, including penetration testing, fault injection, and fuzz testing. Furthermore, the paper examines the integration of formal methods and validation techniques within the automotive development lifecycle, including requirements engineering, design, implementation, and testing phases. It discusses the benefits and limitations of these approaches, considering factors such as scalability, efficiency, and applicability to real-world automotive systems. Through an extensive review of relevant literature and case studies, this survey provides insights into the current research trends, challenges, and open research questions in the field of formal methods and validation techniques for automotive system security. The findings of this survey can serve as a valuable resource for researchers, practitioners, and policymakers involved in the design, development, and evaluation of secure automotive systems.

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Section: Abstract Interpretationmentioning

confidence: 99%

Formal Methods and Validation Techniques for Ensuring Automotive Systems Security

Krichen

2023

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“…However, since indoor temperature fluctuations over such short intervals are minimal, it may be unnecessary to include them in temperature prediction models. Therefore, we propose using numerical abstraction techniques [32] to approximate larger time intervals of the home's environment. This approach helps to reduce the data size for more efficient training and energy-saving during model development.…”

Section: Temporal Abstractionmentioning

confidence: 99%

PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Boubouh,

Basmadjian,

Ardakanian

et al. 2023

Energies

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Nowadays, the integration of home automation systems with smart thermostats is a common trend, designed to enhance resident comfort and conserve energy. The introduction of smart thermostats that can run machine learning algorithms has opened the door for on-device training, enabling customized thermal experiences in homes. However, leveraging the flexibility offered by on-device learning has been hindered by the absence of a tailored learning scheme that allows for accurate on-device training of thermal models. Traditional centralized learning (CL) and federated learning (FL) schemes rely on a central server that controls the learning experience, compromising the home’s privacy and requiring significant energy to operate. To address these challenges, we propose PePTM, a personalized peer-to-peer thermal modeling algorithm that generates tailored thermal models for each home, offering a controlled learning experience with a minimal training energy footprint while preserving the home’s privacy, an aspect difficult to achieve in both CL and FL. PePTM consists of local and collaborative learning phases that enable each home to train its thermal model and collaboratively improve it with a set of similar homes in a peer-to-peer fashion. To showcase the effectiveness of PePTM, we use a year’s worth of data from US homes to train thermal models using the RNN time-series model and compare the data across three learning schemes: CL, FL, and PePTM, in terms of model performance and the training energy footprint. Our experimental results show that PePTM is significantly energy-efficient, requiring 695 and 40 times less training energy than CL and FL, respectively, while maintaining comparable performance. We believe that PePTM sets the stage for new avenues for on-device thermal model training, providing a personalized thermal experience with reduced energy consumption and enhanced privacy.

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“…Much work have since, although perhaps implicitly, relied on their formulation. Often probabilistic descriptions are known with imprecision that manifests as non-deterministic uncertainty [2]. Adje et al [1] introduced an abstraction based on the zonotope abstraction for Dempster-Shafer structures and P-boxes 10 .…”

Section: Related Workmentioning

confidence: 99%

“…In the context of fuzzy logic, crisp or Boolean set refer to a classical set to avoid confusion with fuzzy sets 2. Although one would expect the definition of a fuzzy logic to include a "fuzzy implication" operator in this work we do not consider it.…”

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confidence: 99%

Bridging Static and Dynamic Program Analysis using Fuzzy Logic

Lidman

Svenningsson

2017

Electron. Proc. Theor. Comput. Sci.

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Static program analysis is used to summarize properties over all dynamic executions. In a unifying approach based on 3-valued logic properties are either assigned a definite value or unknown. But in summarizing a set of executions, a property is more accurately represented as being biased towards true, or towards false. Compilers use program analysis to determine benefit of an optimization. Since benefit (e.g., performance) is justified based on the common case understanding bias is essential in guiding the compiler. Furthermore, successful optimization also relies on understanding the quality of the information, i.e. the plausibility of the bias. If the quality of the static information is too low to form a decision we would like a mechanism that improves dynamically.We consider the problem of building such a reasoning framework and present the fuzzy data-flow analysis. Our approach generalize previous work that use 3-valued logic. We derive fuzzy extensions of data-flow analyses used by the lazy code motion optimization and unveil opportunities previous work would not detect due to limited expressiveness. Furthermore we show how the results of our analysis can be used in an adaptive classifier that improve as the application executes.

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Abstract interpretation

Cited by 38 publications

References 192 publications

Formal Methods and Validation Techniques for Ensuring Automotive Systems Security

Formal Methods and Validation Techniques for Ensuring Automotive Systems Security

PePTM: An Efficient and Accurate Personalized P2P Learning Algorithm for Home Thermal Modeling

Bridging Static and Dynamic Program Analysis using Fuzzy Logic

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