42 research outputs found
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 fĂŒr das zeitabhĂ€ngige Argumentieren ĂŒber reaktive Systeme. In Teil I werden die Grenzen der Entscheidbarkeit des ErfĂŒllbarkeits- und des Realisierbarkeitsproblem von temporalen Hypereigenschaften bestimmt. Temporale Hypereigenschaften setzen mehrere Berechnungsspuren zueinander in Beziehung und werden in einer temporalen Hyperlogik ausgedrĂŒckt. Insbesondere werden entscheidbare Fragmente der hochexpressiven Hyperlogiken HyperQPTL und HyperCTL* identifiziert. Als Anwendung wird ein Enforcement-Mechanismus fĂŒr temporale Hypereigenschaften erarbeitet. Explizite Enforcement-Algorithmen fĂŒr 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 groĂe Mengen zusĂ€tzlicher 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 fĂŒr Formeln aus Benchmarks aus der Literatur vorhersagen, bei denen der klassische Solver eine ZeitĂŒberschreitung hatte. ZusĂ€tzlich wird gezeigt, dass das neuronale Netz einen erheblichen Teil der Probleme aus dem jĂ€hrlichen Synthesewettbewerb (SYNTCOMP) und sogar Beispiele auĂerhalb der Distribution aus einer aktuellen Fallstudie lösen kann
Solving Infinite-State Games via Acceleration
Two-player graph games have found numerous applications, most notably in the
synthesis of reactive systems from temporal specifications, but also in
verification. The relevance of infinite-state systems in these areas has lead
to significant attention towards developing techniques for solving
infinite-state games.
We propose novel symbolic semi-algorithms for solving infinite-state games
with -regular winning conditions. The novelty of our approach lies in
the introduction of an acceleration technique that enhances fixpoint-based
game-solving methods and helps to avoid divergence. Classical fixpoint-based
algorithms, when applied to infinite-state games, are bound to diverge in many
cases, since they iteratively compute the set of states from which one player
has a winning strategy. Our proposed approach can lead to convergence in cases
where existing algorithms require an infinite number of iterations. This is
achieved by acceleration: computing an infinite set of states from which a
simpler sub-strategy can be iterated an unbounded number of times in order to
win the game. Ours is the first method for solving infinite-state games to
employ acceleration. Thanks to this, it is able to outperform state-of-the-art
techniques on a range of benchmarks, as evidenced by our evaluation of a
prototype implementation
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system
development and have played a key role in establishing the correctness of
safety-critical systems. The main building blocks of formal methods are models
and specifications, which are analogous to behaviors and requirements in system
design and give us the means to verify and synthesize system behaviors with
formal guarantees.
This monograph provides a survey of the current state of the art on
applications of formal methods in the autonomous systems domain. We consider
correct-by-construction synthesis under various formulations, including closed
systems, reactive, and probabilistic settings. Beyond synthesizing systems in
known environments, we address the concept of uncertainty and bound the
behavior of systems that employ learning using formal methods. Further, we
examine the synthesis of systems with monitoring, a mitigation technique for
ensuring that once a system deviates from expected behavior, it knows a way of
returning to normalcy. We also show how to overcome some limitations of formal
methods themselves with learning. We conclude with future directions for formal
methods in reinforcement learning, uncertainty, privacy, explainability of
formal methods, and regulation and certification
Project Final Report Use and Dissemination of Foreground
This document is the final report on use and dissemination of foreground, part of the CONNECT final report. The document provides the lists of: publications, dissemination activities, and exploitable foregroun
Security Analysis of System Behaviour - From "Security by Design" to "Security at Runtime" -
The Internet today provides the environment for novel applications and
processes which may evolve way beyond pre-planned scope and
purpose. Security analysis is growing in complexity with the increase
in functionality, connectivity, and dynamics of current electronic
business processes. Technical processes within critical
infrastructures also have to cope with these developments. To tackle
the complexity of the security analysis, the application of models is
becoming standard practice. However, model-based support for security
analysis is not only needed in pre-operational phases but also during
process execution, in order to provide situational security awareness
at runtime.
This cumulative thesis provides three major contributions to modelling
methodology.
Firstly, this thesis provides an approach for model-based analysis and
verification of security and safety properties in order to support
fault prevention and fault removal in system design or redesign.
Furthermore, some construction principles for the design of
well-behaved scalable systems are given.
The second topic is the analysis of the exposition of vulnerabilities
in the software components of networked systems to exploitation by
internal or external threats. This kind of fault forecasting allows
the security assessment of alternative system configurations and
security policies. Validation and deployment of security policies
that minimise the attack surface can now improve fault tolerance and
mitigate the impact of successful attacks.
Thirdly, the approach is extended to runtime applicability. An
observing system monitors an event stream from the observed system
with the aim to detect faults - deviations from the specified
behaviour or security compliance violations - at runtime.
Furthermore, knowledge about the expected behaviour given by an
operational model is used to predict faults in the near
future. Building on this, a holistic security management strategy is
proposed. The architecture of the observing system is described and
the applicability of model-based security analysis at runtime is
demonstrated utilising processes from several industrial scenarios.
The results of this cumulative thesis are provided by 19 selected
peer-reviewed papers
Formal Specification and Verification for Automated Production Systems
Complex industrial control software often drives safety- and mission-critical
systems, like automated production plants or control units embedded into devices in automotive systems. Such controllers have in common that they are reactive systems, i.e., that they periodically read sensor stimuli and cyclically execute the same program to produce actuator signals.
The correctness of software for automated production is rarely verified using
formal techniques. Although, due to the Industrial Revolution 4.0 (IR4.0), the
impact and importance of software have become an important role in industrial automation.
What is used instead in industrial practice today is testing and simulation,
where individual test cases are used to validate an automated production system.
Three reasons why formal methods are not popular are: (a) It is difficult to
adequately formulate the desired temporal properties. (b) There is a lack of
specification languages for reactive systems that are both sufficiently
expressive and comprehensible for practitioners. (c) Due to the lack of an
environment model the obtained results are imprecise. Nonetheless, formal
methods for automated production systems are well studied academically---mainly on the verification of safety properties via model checking.
In this doctoral thesis we present the concept of (1) generalized test tables
(GTTs), a new specification language for functional properties, and their
extension (2) relational test tables (RTTs) for relational properties. The
concept includes the syntactical notion, designed for the intuition of
engineers, and the semantics, which are based on game theory. We use RTTs for a novel confidential property on reactive systems, the provably forgetting of information. Moreover, for regression verification, an important relational
property, we are able to achieve performance improvements by (3) creating
a decomposing rule which splits large proofs into small sub-task. We implemented the verification procedures and evaluated them against realistic case studies, e.g., the Pick-and-Place-Unit from the Technical University of Munich.
The presented contribution follows the idea of lowering the obstacle of
verifying the dependability of reactive systems in general, and automated
production systems in particular for the engineer either by introducing a new
specification language (GTTs), by exploiting existing programs for the
specification (RTTs, regression verification), or by improving the verification
performance