Abstract The Move Prover () is a formal verifier for smart contracts written in the Move programming language. has an expressive specification language, and is fast and reliable enough that it can be run routinely by developers and in integration testing. Besides the simplicity of smart contracts and the Move language, three implementation approaches are responsible for the practicality of : (1) an alias-free memory model, (2) fine-grained invariant checking, and (3) monomorphization. The entirety of the Move code for the Diem blockchain has been extensively specified and can be completely verified by in a few minutes. Changes in the Diem framework must be successfully verified before being integrated into the open source repository on GitHub.

The Libra blockchain is designed to store billions of dollars in assets, so the security of code that executes transactions is important. The Libra blockchain has a new language for implementing transactions, called “Move.” This paper describes the Move Prover, an automatic formal verification system for Move. We overview the unique features of the Move language and then describe the architecture of the Prover, including the language for formal specification and the translation to the Boogie intermediate verification language .

In the model-based development of controller software, the use of an unverified code generator/transformer may result in introducing unintended bugs in the controller implementation. To assure the correctness of the controller software in the absence of verified code generator/transformer, we develop Linear Controller Verifier (LCV), a tool to verify a linear controller implementation against its original linear controller model. LCV takes as input a Simulink block diagram model and a C code implementation, represents them as linear time-invariant system models respectively, and verifies an input-output equivalence between them. We demonstrate that LCV successfully detects a known bug of a widely used code generator and an unknown bug of a code transformer. We also demonstrate the scalability of LCV and a real-world case study with the controller of a quadrotor system.

This article is concerned with the security of modern Cyber-Physical Systems in the presence of transient sensor faults. We consider a system with multiple sensors measuring the same physical variable, where each sensor provides an interval with all possible values of the true state. We note that some sensors might output faulty readings and others may be controlled by a malicious attacker. Differing from previous works, in this article, we aim to distinguish between faults and attacks and develop an attack detection algorithm for the latter only. To do this, we note that there are two kinds of faults—transient and permanent; the former are benign and short-lived, whereas the latter may have dangerous consequences on system performance. We argue that sensors have an underlying transient fault model that quantifies the amount of time in which transient faults can occur. In addition, we provide a framework for developing such a model if it is not provided by manufacturers. Attacks can manifest as either transient or permanent faults depending on the attacker’s goal. We provide different techniques for handling each kind. For the former, we analyze the worst-case performance of sensor fusion over time given each sensor’s transient fault model and develop a filtered fusion interval that is guaranteed to contain the true value and is bounded in size. To deal with attacks that do not comply with sensors’ transient fault models, we propose a sound attack detection algorithm based on pairwise inconsistencies between sensor measurements. Finally, we provide a real-data case study on an unmanned ground vehicle to evaluate the various aspects of this article.

Since Cyber Physical Systems (CPS) are widely used inmany safety-critical domains these days, critical properties such as robustness and resilience are required for such systems. To increase the robustness and the resilience of CPS, various sensor fusion techniques have been studied [1], [2],[3], [4]. These fusion techniques are based on certain sensor models, which broadly fall into two categories: probabilistic model and abstract model. The probabilistic sensor model [1] uses certain noise distributions on sensors (e.g., Gaussian), which is wellsuited for analyzing the systems' expected performance inthe average case. However, wrong assumptions on noise distributions may be in danger of being vulnerable to sensor attacks. On the other hand, the abstract sensor model [2] usesthe worst-case error bound of sensors. Thus, this model iswell suited for the systems' worst-case performance, whichis highly relevant to the case of sensor attacks [3].In this work, we study a hybrid sensor fusion that usesboth probabilistic and abstract sensor models to be able tobenefit from both. We demonstrate the validation of ourhybrid sensor fusion technique using an unmanned groundvehicle called Jackal.

We consider the problem of verification of software implementations of linear time-invariant controllers. Commonly, different implementations use different representations of the controller's state, for example due to optimizations in a third-party code generator. To accommodate this variation, we exploit input-output controller specification captured by the controller's transfer function and show how to automatically verify correctness of C code controller implementations using a Frama-C/Why3/Z3 toolchain. Scalability of the approach is evaluated using randomly generated controller specifications of realistic size.