4,435 research outputs found
ADsafety: Type-Based Verification of JavaScript Sandboxing
Web sites routinely incorporate JavaScript programs from several sources into
a single page. These sources must be protected from one another, which requires
robust sandboxing. The many entry-points of sandboxes and the subtleties of
JavaScript demand robust verification of the actual sandbox source. We use a
novel type system for JavaScript to encode and verify sandboxing properties.
The resulting verifier is lightweight and efficient, and operates on actual
source. We demonstrate the effectiveness of our technique by applying it to
ADsafe, which revealed several bugs and other weaknesses.Comment: in Proceedings of the USENIX Security Symposium (2011
Size Matters: Microservices Research and Applications
In this chapter we offer an overview of microservices providing the
introductory information that a reader should know before continuing reading
this book. We introduce the idea of microservices and we discuss some of the
current research challenges and real-life software applications where the
microservice paradigm play a key role. We have identified a set of areas where
both researcher and developer can propose new ideas and technical solutions.Comment: arXiv admin note: text overlap with arXiv:1706.0735
Statically Checking Web API Requests in JavaScript
Many JavaScript applications perform HTTP requests to web APIs, relying on
the request URL, HTTP method, and request data to be constructed correctly by
string operations. Traditional compile-time error checking, such as calling a
non-existent method in Java, are not available for checking whether such
requests comply with the requirements of a web API. In this paper, we propose
an approach to statically check web API requests in JavaScript. Our approach
first extracts a request's URL string, HTTP method, and the corresponding
request data using an inter-procedural string analysis, and then checks whether
the request conforms to given web API specifications. We evaluated our approach
by checking whether web API requests in JavaScript files mined from GitHub are
consistent or inconsistent with publicly available API specifications. From the
6575 requests in scope, our approach determined whether the request's URL and
HTTP method was consistent or inconsistent with web API specifications with a
precision of 96.0%. Our approach also correctly determined whether extracted
request data was consistent or inconsistent with the data requirements with a
precision of 87.9% for payload data and 99.9% for query data. In a systematic
analysis of the inconsistent cases, we found that many of them were due to
errors in the client code. The here proposed checker can be integrated with
code editors or with continuous integration tools to warn programmers about
code containing potentially erroneous requests.Comment: International Conference on Software Engineering, 201
Quantitative Verification: Formal Guarantees for Timeliness, Reliability and Performance
Computerised systems appear in almost all aspects of our daily lives, often in safety-critical scenarios such as embedded control systems in cars and aircraft
or medical devices such as pacemakers and sensors. We are thus increasingly reliant on these systems working correctly, despite often operating in unpredictable or unreliable environments. Designers of such devices need ways to guarantee that they will operate in a reliable and efficient manner.
Quantitative verification is a technique for analysing quantitative aspects of a system's design, such as timeliness, reliability or performance. It applies formal methods, based on a rigorous analysis of a mathematical model of the system, to automatically prove certain precisely specified properties, e.g. ``the airbag will always deploy within 20 milliseconds after a crash'' or ``the probability of both sensors failing simultaneously is less than 0.001''.
The ability to formally guarantee quantitative properties of this kind is beneficial across a wide range of application domains. For example, in safety-critical systems, it may be essential to establish credible bounds on the probability with which certain failures or combinations of failures can occur. In embedded control systems, it is often important to comply with strict constraints on timing or resources. More generally, being able to derive guarantees on precisely specified levels of performance or efficiency is a valuable tool in the design of, for example, wireless networking protocols, robotic systems or power management algorithms, to name but a few.
This report gives a short introduction to quantitative verification, focusing in particular on a widely used technique called model checking, and its generalisation to the analysis of quantitative aspects of a system such as timing, probabilistic behaviour or resource usage.
The intended audience is industrial designers and developers of systems such as those highlighted above who could benefit from the application of quantitative verification,but lack expertise in formal verification or modelling
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A Static Verification Framework for Secure Peer-to-Peer Applications
In this paper we present a static verification framework to support the design and verification of secure peer-to-peer applications. The framework supports the specification, modeling, and analysis of security aspects together with the general characteristics of the system, during early stages of the development life-cycle. The approach avoids security issues to be taken into consideration as a separate layer that is added to the system as an afterthought by the use of security protocols. The main functionality supported by the framework are concerned with the modeling of the system together with its security aspects by using an extension of UML, modeling of abuse cases to represent scenarios of attackers and assist with the identification of properties to be verified, specification of properties to be verified in a graphical template language, verification of the models against the properties, and visualization of the results of the verification process
IoTSan: Fortifying the Safety of IoT Systems
Today's IoT systems include event-driven smart applications (apps) that
interact with sensors and actuators. A problem specific to IoT systems is that
buggy apps, unforeseen bad app interactions, or device/communication failures,
can cause unsafe and dangerous physical states. Detecting flaws that lead to
such states, requires a holistic view of installed apps, component devices,
their configurations, and more importantly, how they interact. In this paper,
we design IoTSan, a novel practical system that uses model checking as a
building block to reveal "interaction-level" flaws by identifying events that
can lead the system to unsafe states. In building IoTSan, we design novel
techniques tailored to IoT systems, to alleviate the state explosion associated
with model checking. IoTSan also automatically translates IoT apps into a
format amenable to model checking. Finally, to understand the root cause of a
detected vulnerability, we design an attribution mechanism to identify
problematic and potentially malicious apps. We evaluate IoTSan on the Samsung
SmartThings platform. From 76 manually configured systems, IoTSan detects 147
vulnerabilities. We also evaluate IoTSan with malicious SmartThings apps from a
previous effort. IoTSan detects the potential safety violations and also
effectively attributes these apps as malicious.Comment: Proc. of the 14th ACM CoNEXT, 201
Modular Verification of Interrupt-Driven Software
Interrupts have been widely used in safety-critical computer systems to
handle outside stimuli and interact with the hardware, but reasoning about
interrupt-driven software remains a difficult task. Although a number of static
verification techniques have been proposed for interrupt-driven software, they
often rely on constructing a monolithic verification model. Furthermore, they
do not precisely capture the complete execution semantics of interrupts such as
nested invocations of interrupt handlers. To overcome these limitations, we
propose an abstract interpretation framework for static verification of
interrupt-driven software that first analyzes each interrupt handler in
isolation as if it were a sequential program, and then propagates the result to
other interrupt handlers. This iterative process continues until results from
all interrupt handlers reach a fixed point. Since our method never constructs
the global model, it avoids the up-front blowup in model construction that
hampers existing, non-modular, verification techniques. We have evaluated our
method on 35 interrupt-driven applications with a total of 22,541 lines of
code. Our results show the method is able to quickly and more accurately
analyze the behavior of interrupts.Comment: preprint of the ASE 2017 pape
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