The Solidity smart contract language is deterministic by design. Nevertheless, some instances of nondeterminism (ND) in smart contracts stem from unpredictable inputs received during execution, such as inputs from asynchronous callbacks of Oracles or externally called contracts that halt unexpectedly. Such values must be validated to meet prespecified criteria before they reach critical program parts; otherwise, they can lead to nondeterministic state outcomes in smart contracts, which we refer to as ND-issues. To detect ND-issues, we propose an information flow-based analysis that tracks the unpredictable inputs and return values that are not validated before reaching critical program parts. We have implemented our proposal in FONOVA and evaluated it using 326 frequently used Ethereum smart contracts. The evaluation shows that FONOVA detects five times more exploitable instances of ND-issues with 12 times shorter analysis time than leading tools like Ethainter, Securify, and Mythril. © 2024 IEEE.

Smart contracts are programs with mutable state. Transactions submitted to these contracts trigger functions that often modify state. Nodes of the Ethereum blockchain schedule such transactions in a nondeterministic order, potentially leading to races between transactions and concurrency issues. When the outcome of a smart contract varies depending on the order in which transactions are processed, we have a Transaction Ordering Dependency (TOD). TOD enables malicious actors to profit from a smart contract, similar to the well-known frontrunning vulnerability. Existing approaches for detecting TOD in Ethereum smart contracts yield a high rate of false positives and false negatives. To help contract developers and testers detect TOD vulnerabilities with enhanced precision, we propose and evaluate an analysis based on information flow in our tool TODChecker1 . We evaluate our approach using a benchmark comprising 513 vulnerable transactions involving 235 real-world Ethereum smart contracts susceptible to frontrunning attacks. Our evaluation finds that our approach outperforms existing approaches, including Oyente, Securify, SAILFISH, TODler, and Nyx, in precision, runtime, and in identifying novel TOD vulnerabilities. © 2024 Copyright held by the owner/author(s)

In this concise yet comprehensive Open Access textbook, future inventors are introduced to the key concepts of Cyber-Physical Systems (CPS). Using modeling as a way to develop deeper understanding of the computational and physical components of these systems, one can express new designs in a way that facilitates their simulation, visualization, and analysis. Concepts are introduced in a cross-disciplinary way. Leveraging hybrid (continuous/discrete) systems as a unifying framework and Acumen as a modeling environment, the book bridges the conceptual gap in modeling skills needed for physical systems on the one hand and computational systems on the other. In doing so, the book gives the reader the modeling and design skills they need to build smart, IT-enabled products. Starting with a look at various examples and characteristics of Cyber-Physical Systems, the book progresses to explain how the area brings together several previously distinct ones such as Embedded Systems, Control Theory, and Mechatronics. Featuring a simulation-based project that focuses on a robotics problem (how to design a robot that can play ping-pong) as a useful example of a CPS domain, Cyber-Physical Systems: A Model-Based Approach demonstrates the intimate coupling between cyber and physical components, and how designing robots reveals several non-trivial control problems, significant embedded and real-time computation requirements, and a need to consider issues of communication and preconceptions. © The Editor(s) (if applicable) and The Author(s) 2021

Hybrid systems are dynamical systems of both continuous and discrete nature and constitute an important field of control systems theory and engineering. On the other hand, intelligent data processing has become one of the most critical devices of modern computer based systems as these systems operate in environments featuring increasing uncertainty and unpredictability. While these two approaches set completely different objectives, modern cyber-physical systems, taken as variants of hybrid systems, seem to constitute a field of increasing interest for applying intelligent techniques. Moreover, the examples of, not so recent, intelligent control systems are suggestive for considering a study on getting intelligent techniques close to hybrid systems. In this paper we present the experimental investigation we undertook in this direction. More specifically, we present and discuss the experiments carried out using Acumen a hybrid systems modeling and simulation environment. Without urging towards setting and solving questions of conceptual order we tried to figure out whether it is possible to represent intelligent behavior using a tool for modeling dynamical systems focusing on the study of its ability to permit the representation of both continuous and discrete intelligent techniques, namely, Reinforcement Learning and Hopfield neural networks. The results obtained are indicative of the problems related to the specific computational context and are useful in deriving conclusions concerning the functionality that needs to be provided by such modeling and simulation environments, in order to allow for the coexistence of hybrid systems and intelligent techniques. © 2021, IFIP International Federation for Information Processing

Industrial cyber-physical system products interleave hardware, software, and communication components. System complexity is increasing simultaneously with increased demands on quality and shortened time-to-market. To effectively support the development of such systems, we present languages and tools for comprehensive integrated model-based development that cover major phases such as requirement analysis, design, implementation, and maintenance. The model-based approach raises the level of abstraction and allows to perform virtual prototyping by simulating and optimizing system models before building physical products. Moreover, open standards and open source implementations enable model portability, tool reuse and a broader deployment. In this paper we present a general overview of the available solutions with focus on Modelica/OpenModelica, Bloqqi, and Acumen. The paper presents contributions to these languages and environments, including symbolic-numeric modeling, requirement verification, code generation, model debugging, design optimization, graphical modeling, and variant handling with traceability, as well a general discussion and conclusions. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Safety analysis of high confidence systems requires guaranteed bounds on the probabilities of events of interest. Establishing the correctness of algorithms that aim to compute such bounds is challenging. We address this problem in three steps. First, we use monadic transition systems (MTS) in the category of sets as a framework for modeling discrete time systems. MTS can capture different types of system behaviors, but we focus on a combination of non-deterministic and probabilistic behaviors that often arises when modeling complex systems. Second, we use the category of posets and monotonic maps as a setting to define and compare approximations. In particular, for the MTS of interest, we consider approximations of their configurations based on complete lattices. Third, by restricting to finite lattices, we obtain algorithms that compute over-approximations, i.e., bounds from above within some partial order of approximants, of the system configuration after n steps. Interestingly, finite lattices of “interval probabilities” may fail to accurately approximate configurations that are both non-deterministic and probabilistic, even for deterministic (and continuous) system dynamics. However, better choices of finite lattices are available. © 2020 University of Szeged, Institute of Informatics. All rights reserved.

This book constitutes the proceedings of the 8th International Workshop on Design, Modeling, and Evaluation of Cyber Physical Systems, CyPhy 2018 and 14th International Workshop on Embedded and Cyber-Physical Systems Education, WESE 2018, held in conjunction with ESWeek 2018, in Torino, Italy, in October 2018. The 13 full papers presented together  with 1 short paper in this volume were carefully reviewed and selected from 18 submissions. The conference presents a wide range of domains including Modeling, simulation, verification, design, cyber-physical systems, embedded systems, real-time systems, safety, and reliability. © 2019 Springer Nature Switzerland AG. Part of Springer Nature.

Intelligent Transportation Systems (ITS) are an excellent illustration of the types of challenges that future technologists must address. In previous work we presented a course designed to engage students with theoretical aspects of embedded and cyber-physical systems. In this paper we present MicroITS, a platform addressing applied aspects. We articulate the design goals that we believe are needed to achieve engagement in an educational setting, and describe the platform and its baseline functionality. We briefly describe example projects that can be realized using MicroITS. Our hope is that this report will encourage the development of a community of educators and students interested in the use and the continued development of the platform. © Springer Nature Switzerland AG 2019.

Software is increasingly embedded in a variety of physical contexts. This imposes new requirements on tools that support the design and analysis of systems. For instance, modeling embedded and cyberphysical systems needs to blend discrete mathematics, which is suitable for modeling digital components, with continuous mathematics, used for modeling physical components. This blending of continuous and discrete creates challenges that are absent when the discrete or the continuous setting are considered in isolation. We consider robustness, that is, the ability of an analysis of a model to cope with small amounts of imprecision in the model. Formally, we identify analyses with monotonic maps between complete lattices (a mathematical framework used for abstract interpretation and static analysis) and define robustness for monotonic maps between complete lattices of closed subsets of a metric space. Copyright © 2019 for this paper by its authors.

Software is increasingly embedded in a variety of physical contexts. This imposes new requirements on tools that support the design and analysis of systems. For instance, modeling embedded and cyber-physical systems needs to blend discrete mathematics, which is suitable for modeling digital components, with continuous mathematics, used for modeling physical components. This blending of continuous and discrete creates challenges that are absent when the discrete or the continuous setting are considered in isolation. We consider robustness, that is, the ability of an analysis of a model to cope with small amounts of imprecision in the model. Formally, we identify analyses with monotonic maps between complete lattices (a mathematical framework used for abstract interpretation and static analysis) and define robustness for monotonic maps between complete lattices of closed subsets of a metric space. © Springer Nature Switzerland AG 2019