Projektbeschreibung
Automatenlernen für die Netzwerkverifikation
Damit Systeme das gewünschte Verhalten zeigen sowie leistungsfähig und sicher sind, müssen in der Informatik entsprechende Methoden und Instrumente entwickelt werden. Bei der Verifikation von Bankkarten und einfachen Netzwerkkommunikationsprotokollen werden Fehler bereits mithilfe des Automatenlernens gefunden. Aktuelle Algorithmen unterstützen jedoch nicht die quantitativen oder gleichzeitigen Aspekte, die zur Modellierung von Eigenschaften wie der Netzwerküberlastung oder Fehlertoleranz notwendig sind. Das EU-finanzierte Projekt AutoProbe wird einen neuen Verifikationsrahmen entwickeln, der eine automatisierte modellbasierte Verifikation für probabilistische und parallele Systeme ermöglicht und von Netzwerkanwendungen motiviert wird. Angelehnt an den zukunftsträchtigen L*-Algorithmus von Dana Angluin wird das Projektteam aktive Lernalgorithmen für Automatenmodelle mit probabilistischen und parallelen Eigenschaften bereitstellen.
Ziel
One of the longstanding challenges in Computer Science has been the development of methods and tools providing rigorous guarantees about systems’ behavior, performance, and security. There have been many successes in overcoming this challenge, notably the invention and widespread use of model checking. However, existing methods are impaired by the tension between the need of fast developing systems and the slowdown caused by the complexity of providing a model against which running systems can be verified. Automata learning – automated discovery of automata models from system observations such as test logs – is emerging as a highly effective bug-finding technique with applications in verification of bank cards and basic network communication protocols. The design of algorithms for automata learning is a fundamental research problem and in the last years much progress has been made in developing and understanding of new algorithms (including the PI’s own work). Yet, existing algorithms do not support crucial quantitative or concurrency aspects that are essential in modelling properties such as network congestion and fault-tolerance. The central objective of this project is to develop a new verification framework that enables automated model- based verification for probabilistic and concurrent systems, motivated by applications in networks. We will provide active learning algorithms, in the style of Angluin’s seminal L* algorithm, for automata models that were so far too complex to be tackled. We will base our development on rigorous semantic foundations, developed by the PI in recent years, which provide correctness for the algorithms in a modular way. The project will significantly advance model-based verification in new and previously unexplored directions. This line of research will not only result in fundamental theoretical contributions and insights in their own right but will also impact the practice of concurrent and probabilistic network verification.
Schlüsselbegriffe
Programm/Programme
Thema/Themen
Aufforderung zur Vorschlagseinreichung
(öffnet in neuem Fenster) ERC-2020-COG
Andere Projekte für diesen Aufruf anzeigenFinanzierungsplan
ERC-COG - Consolidator GrantGastgebende Einrichtung
WC1E 6BT London
Vereinigtes Königreich