Prevention of cardiac arrhythmias in pediatric patients with normotensive-hypokalemic tubulopathy: Current attitude among European pediatricians

Potassium deficiency predisposes to cardiac arrhythmias culminating in syncope or sudden death. Because of the uncertainty related to the possible occurrence of such cardiac arrhythmias in the context of normotensive-hypokalemic tubulopathies, 19 European pediatric nephrologists with a large experience of normotensive-hypokalemic tubulopathies were asked to answer a questionnaire. The responses suggest that inherited normotensive-hypokalemic tubulopathies per se do not strongly predispose to dangerous cardiac arrhythmias. However, cardiac arrhythmias may be acutely precipitated by drugs that prolong the QT interval, by diarrhea, or vomiting, and perhaps even by physical activity. Finally, the likelihood of dangerous arrhythmias in normotensive-hypokalemic tubulopathy is currently unknow

Static analysis for dummies: experiencing LiSA

Semantics-based static analysis requires a significant theoretical background before being able to design and implement a new analysis. Unfortunately, the development of even a toy static analyzer from scratch requires to implement an infrastructure (parser, control flow graphs representation, fixpoint algorithms, etc.) that is too demanding for bachelor and master students in computer science. This approach difficulty can condition the acquisition of skills on software verification which are of major importance for the design of secure systems. In this paper, we show how LiSA (Library for Static Analysis) can play a role in that respect. LiSA implements the basic infrastructure that allows a non-expert user to develop even simple analyses (e.g., dataflow and numerical non-relational domains) focusing only on the design of the appropriate representation of the property of interest and of the sound approximation of the program statements

Cross-Programming Language Taint Analysis for the IoT Ecosystem

The Internet of Things (IoT) is a key component for the next disruptive technologies. However, IoT merges together several diverse software layers: embedded, enterprise, and cloud programs interact with each other. In addition, security and privacy vulnerabilities of IoT software might be particularly dangerous due to the pervasiveness and physical nature of these systems. During the last decades, static analysis, and in particular taint analysis, has been widely applied to detect software vulnerabilities. Unfortunately, these analyses assume that software is entirely written in a single programming language, and they are not immediately suitable to detect IoT vulnerabilities where many different software components, written in different programming languages, interact. This paper discusses how to leverage existing static taint analyses to a cross-programming language scenario

Static analysis for discovering IoT vulnerabilities

The Open Web Application Security Project (OWASP), released the \u201cOWASP Top 10 Internet of Things 2018\u201d list of the high-priority security vulnerabilities for IoT systems. The diversity of these vulnerabilities poses a great challenge toward development of a robust solution for their detection and mitigation. In this paper, we discuss the relationship between these vulnerabilities and the ones listed by OWASP Top 10 (focused on Web applications rather than IoT systems), how these vulnerabilities can actually be exploited, and in which cases static analysis can help in preventing them. Then, we present an extension of an industrial analyzer (Julia) that already covers five out of the top seven vulnerabilities of OWASP Top 10, and we discuss which IoT Top 10 vulnerabilities might be detected by the existing analyses or their extension. The experimental results present the application of some existing Julia\u2019s analyses and their extension to IoT systems, showing its effectiveness of the analysis of some representative case studies

SAILS: static analysis of information leakage with Sample

ABSTRACT In this paper, we introduce Sails, a new tool that combines Sample, a generic static analyzer, and a sophisticated domain for leakage analysis. This tool does not require to modify the original language, since it works with mainstream languages like Java, and it does not require any manual annotation. Sails can combine the information leakage analysis with different heap abstractions, inferring information leakage over programs dealing with complex data structures. We applied Sails to the analysis of the SecuriBench-micro suite. The experimental results show the effectiveness of our approach

CIL to Java-bytecode translation for static analysis leveraging

A formal translation of CIL (i.e., .Net) bytecode into Java bytecode is introduced and proved sound with respect to the language semantics. The resulting code is then analyzed with Julia, an industrial static analyzer of Java bytecode. The overall process of translation and analysis is fast, scales up to industrial programs, and introduces a negligible number of false alarms. The main result of this work is to leverage existing, mature, and sound analyzers for Java bytecode by applying them to the (translated) CIL bytecode

Information Flow Analysis for Detecting Non-Determinism in Blockchain

A mandatory feature for blockchain software, such as smart contracts and decentralized applications, is determinism. In fact, non-deterministic behaviors do not allow blockchain nodes to reach one common consensual state or a deterministic response, which causes the blockchain to be forked, stopped, or to deny services. While domain-specific languages are deterministic by design, general purpose languages widely used for the development of smart contracts such as Go, provide many sources of non-determinism. However, not all non-deterministic behaviours are critical. In fact, only those that affect the state or the response of the blockchain can cause problems, as other uses (for example, logging) are only observable by the node that executes the application and not by others. Therefore, some frameworks for blockchains, such as Hyperledger Fabric or Cosmos SDK, do not prohibit the use of non-deterministic constructs but leave the programmer the burden of ensuring that the blockchain application is deterministic. In this paper, we present a flow-based approach to detect non-deterministic vulnerabilities which could compromise the blockchain. The analysis is implemented in GoLiSA, a semantics-based static analyzer for Go applications. Our experimental results show that GoLiSA is able to detect all vulnerabilities related to non-determinism on a significant set of applications, with better results than other open-source analyzers for blockchain software written in Go

Information Flow Analysis for Detecting Non-Determinism in Blockchain (Artifact)

A mandatory feature for blockchain software, such as smart contracts and decentralized applications, is determinism. In fact, non-deterministic behaviors do not allow blockchain nodes to reach one common consensual state or a deterministic response, which causes the blockchain to be forked, stopped, or to deny services. While domain-specific languages are deterministic by design, general-purpose languages widely used for the development of smart contracts such as Go, provide many sources of non-determinism. However, not all non-deterministic behaviours are critical. In fact, only those that affect the state or the response of the blockchain can cause problems, as other uses (for example, logging) are only observable by the node that executes the application and not by others. Therefore, some frameworks for blockchains, such as Hyperledger Fabric or Cosmos SDK, do not prohibit the use of non-deterministic constructs but leave the programmer the burden of ensuring that the blockchain application is deterministic. In this paper, we present a flow-based approach to detect non-deterministic vulnerabilities which could compromise the blockchain. The analysis is implemented in GoLiSA, a semantics-based static analyzer for Go applications. Our experimental results show that GoLiSA is able to detect all vulnerabilities related to non-determinism on a significant set of applications, with better results than other open-source analyzers for blockchain software written in Go

Combining symbolic and numerical domains for information leakage analysis

We introduce an abstract domain for information-flow analysis of software. The proposal combines variable dependency analysis with numerical abstractions, yielding to accuracy and efficiency improvements. We apply the full power of the proposal to the case of database query languages as well. Finally, we present an implementation of the analysis, called Sails, as an instance of a generic static analyzer. Keeping the modular construction of the analysis, the tool allows one to tune the granularity of heap analysis and to choose the numerical domain involved in the reduced product. This way the user can tune the information leakage analysis at dierent levels of precision and efficiency

Stagnation point heat flux characterization under numerical error and boundary conditions uncertainty

International audienceThe numerical simulation of hypersonic atmospheric entry flows is a challenging problem. Prediction of quantities of interest, such as surface heat flux and pressure, is strongly influenced by the mesh quality using conventional second-order spatial accuracy schemes, while depending on the boundary conditions, which generally suffer from uncertainty. This paper explores these two aspects, illustrating a CFD study on the forebody of the EXPERT vehicle of the European Space Agency employing the US3D solver