Service composition in stochastic settings

With the growth of the Internet-of-Things and online Web services, more services with more capabilities are available to us. The ability to generate new, more useful services from existing ones has been the focus of much research for over a decade. The goal is, given a specification of the behavior of the target service, to build a controller, known as an orchestrator, that uses existing services to satisfy the requirements of the target service. The model of services and requirements used in most work is that of a finite state machine. This implies that the specification can either be satisfied or not, with no middle ground. This is a major drawback, since often an exact solution cannot be obtained. In this paper we study a simple stochastic model for service composition: we annotate the tar- get service with probabilities describing the likelihood of requesting each action in a state, and rewards for being able to execute actions. We show how to solve the resulting problem by solving a certain Markov Decision Process (MDP) derived from the service and requirement specifications. The solution to this MDP induces an orchestrator that coincides with the exact solution if a composition exists. Otherwise it provides an approximate solution that maximizes the expected sum of values of user requests that can be serviced. The model studied although simple shades light on composition in stochastic settings and indeed we discuss several possible extensions

Towards high-level fuzzy control specifications for building automation systems

UID/CEC/50021/2019. Grant: FCT/MCTES TUBITAK/0008/2014 FCT/DAAD - 2018/2019 (Poc. DAAD 441.00). UID/EMS/50022/2019. project TIN2015-73566-JIN and by the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016–2019, ED431G/08 and reference competitive group 2019–2021, ED431C 2018/29) and the European Regional Development Fund (ERDF).The control logic underlying building automation systems has consisted, traditionally, of embedded discrete programs created using either low-level or proprietary scripting languages, or using general purpose fourth-generation visual languages like Simulink. It is also well known that programs developed in this way are hard to evolve, test, and maintain. These difficulties are intensified when continuous control problems have to be tackled or when the actuation must vary continually subject to the sensor inputs. Such is the case in day-lighting or occupancy-based control applications. In this paper, we propose a declarative high-level Domain-Specific Language that aims to reduce the effort required to specify the control logic of building automation systems. Our language combines fuzzy logic and temporal logic, enabling to define the behaviour in terms of domain abstractions. Finally, the approach has been validated in two ways: (i) in a case study that simulates the control system of an automated office room and (ii) by means of an empirical study to confirm usability (with a System Usability Scale questionnaire) and effectiveness, here regarded from the perspective of correctness, of the proposed language with respect to a well-known language like Simulink.authorsversionpublishe

Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities

Recent advances in Internet of Things (IoT) have enabled myriad domains such as smart homes, personal monitoring devices, and enhanced manufacturing. IoT is now pervasive---new applications are being used in nearly every conceivable environment, which leads to the adoption of device-based interaction and automation. However, IoT has also raised issues about the security and privacy of these digitally augmented spaces. Program analysis is crucial in identifying those issues, yet the application and scope of program analysis in IoT remains largely unexplored by the technical community. In this paper, we study privacy and security issues in IoT that require program-analysis techniques with an emphasis on identified attacks against these systems and defenses implemented so far. Based on a study of five IoT programming platforms, we identify the key insights that result from research efforts in both the program analysis and security communities and relate the efficacy of program-analysis techniques to security and privacy issues. We conclude by studying recent IoT analysis systems and exploring their implementations. Through these explorations, we highlight key challenges and opportunities in calibrating for the environments in which IoT systems will be used.Comment: syntax and grammar error are fixed, and IoT platforms are updated to match with the submissio

Synthesis of Specifications and Refinement Maps for Real-Time Object Code Verification

Formal verification methods have been shown to be very effective in finding corner-case bugs and ensuring the safety of embedded software systems. The use of formal verification requires a specification, which is typically a high-level mathematical model that defines the correct behavior of the system to be verified. However, embedded software requirements are typically described in natural language. Transforming these requirements into formal specifications is currently a big gap. While there is some work in this area, we proposed solutions to address this gap in the context of refinement-based verification, a class of formal methods that have shown to be effective for embedded object code verification. The proposed approach also addresses both functional and timing requirements and has been demonstrated in the context of safety requirements for software control of infusion pumps. The next step in the verification process is to develop the refinement map, which is a mapping function that can relate an implementation state (in this context, the state of the object code program to be verified) with the specification state. Actually, constructing refinement maps often requires deep understanding and intuitions about the specification and implementation, it is shown very difficult to construct refinement maps manually. To go over this obstacle, the construction of refinement maps should be automated. As a first step toward the automation process, we manually developed refinement maps for various safety properties concerning the software control operation of infusion pumps. In addition, we identified possible generic templates for the construction of refinement maps. Recently, synthesizing procedures of refinement maps for functional and timing specifications are proposed. The proposed work develops a process that significantly increases the automation in the generation of these refinement maps. The refinement maps can then be used for refinement-based verification. This automation procedure has been successfully applied on the transformed safety requirements in the first part of our work. This approach is based on the identified generic refinement map templates which can be increased in the future as the application required

Logical and deep learning methods for temporal reasoning

In this thesis, we study logical and deep learning methods for the temporal reasoning of reactive systems. In Part I, we determine decidability borders for the satisfiability and realizability problem of temporal hyperproperties. Temporal hyperproperties relate multiple computation traces to each other and are expressed in a temporal hyperlogic. In particular, we identify decidable fragments of the highly expressive hyperlogics HyperQPTL and HyperCTL*. As an application, we elaborate on an enforcement mechanism for temporal hyperproperties. We study explicit enforcement algorithms for specifications given as formulas in universally quantified HyperLTL. In Part II, we train a (deep) neural network on the trace generation and realizability problem of linear-time temporal logic (LTL). We consider a method to generate large amounts of additional training data from practical specification patterns. The training data is generated with classical solvers, which provide one of many possible solutions to each formula. We demonstrate that it is sufficient to train on those particular solutions such that the neural network generalizes to the semantics of the logic. The neural network can predict solutions even for formulas from benchmarks from the literature on which the classical solver timed out. Additionally, we show that it solves a significant portion of problems from the annual synthesis competition (SYNTCOMP) and even out-of-distribution examples from a recent case study.Diese Arbeit befasst sich mit logischen Methoden und mehrschichtigen Lernmethoden für das zeitabhängige Argumentieren über reaktive Systeme. In Teil I werden die Grenzen der Entscheidbarkeit des Erfüllbarkeits- und des Realisierbarkeitsproblem von temporalen Hypereigenschaften bestimmt. Temporale Hypereigenschaften setzen mehrere Berechnungsspuren zueinander in Beziehung und werden in einer temporalen Hyperlogik ausgedrückt. Insbesondere werden entscheidbare Fragmente der hochexpressiven Hyperlogiken HyperQPTL und HyperCTL* identifiziert. Als Anwendung wird ein Enforcement-Mechanismus für temporale Hypereigenschaften erarbeitet. Explizite Enforcement-Algorithmen für Spezifikationen, die als Formeln in universell quantifiziertem HyperLTL angegeben werden, werden untersucht. In Teil II wird ein (mehrschichtiges) neuronales Netz auf den Problemen der Spurgenerierung und Realisierbarkeit von Linear-zeit Temporallogik (LTL) trainiert. Es wird eine Methode betrachtet, um aus praktischen Spezifikationsmustern große Mengen zusätzlicher Trainingsdaten zu generieren. Die Trainingsdaten werden mit klassischen Solvern generiert, die zu jeder Formel nur eine von vielen möglichen Lösungen liefern. Es wird gezeigt, dass es ausreichend ist, an diesen speziellen Lösungen zu trainieren, sodass das neuronale Netz zur Semantik der Logik generalisiert. Das neuronale Netz kann Lösungen sogar für Formeln aus Benchmarks aus der Literatur vorhersagen, bei denen der klassische Solver eine Zeitüberschreitung hatte. Zusätzlich wird gezeigt, dass das neuronale Netz einen erheblichen Teil der Probleme aus dem jährlichen Synthesewettbewerb (SYNTCOMP) und sogar Beispiele außerhalb der Distribution aus einer aktuellen Fallstudie lösen kann

Access control for IoT environments: specification and analysis

2021 Spring.Includes bibliographical references.Smart homes have devices which are prone to attacks as seen in the 2016 Mirai botnet attacks. Authentication and access control form the first line of defense. Towards this end, we propose an attribute-based access control framework for smart homes that is inspired by the Next Generation Access Control (NGAC) model. Policies in a smart home can be complex. Towards this end, we demonstrate how the formal modeling language Alloy can be used for policy analysis. In this work we formally define an IoT environment, express an example security policy in the context of a smart home, and show the policy analysis using Alloy. This work introduces processes for identifying conflicting and redundant rules with respect to a given policy. This work also demonstrates a practical use case for the processes described. In other words, this work formalizes policy rule definition, home IoT environment definition, and rule analysis all in the context of NGAC and Alloy