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Verification and Software Engineering ​

The growing pervasiveness of computerised systems makes our society vulnerable to faults or attacks on such systems. Rigorous software engineering methods and supporting efficient verification tools are crucial to counter this threat.

The objectives are:

  1. to develop rich mathematically rigorous modelling and analysis frameworks for the behaviour of complex distributed software systems. The frameworks should allow functional correctness, resource efficient and security aspects – as well as their tradeoffs – to be captured and analysed,
  2. to develop and implement supporting verification and analysis tools that scale to the growing complexity software systems, and
  3. to transfer frameworks and tools into industrially used development practice.

The research within Verification and Software Engineering complements and exploits techniques from other research themes. In particular, verification may be applied establish security properties of Secure Multiparty Computations of Blockchains as found in the Cybersecurity and Blockchain theme as already demonstrated by the Center for Program Verification. The dependability and robustness of several CPS and IoT-based systems may be established by Verification and Software Engineering methods.

Artificial Intelligence is increasingly exploited to accelerate and scale current verification techniques. Dually, machine-learned components, such as Deep Neural Networks, are challenging existing algorithmic techniques for verification with exciting research emerging these days towards verifiable and explainable AI.

Projects

Bridge project

Verified Voting Protocols and Blockchains

There is constant interest for Internet Voting by election commissions around the world. At the same time, there is a need for online voting in blockchain governance. However, building an internet voting system is not easy: The design of new cryptographic protocols is error-prone, and public trust in the elected body is easily threatened. This project aims to improve the security and quality of the internet voting system and influence regulation on minimum quality requirements for blockchains.

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Bridge project

Verifiable and Safe AI for Autonomous Systems

The rapidly growing application of machine learning techniques in cyber-physical systems leads to better solutions and products in terms of adaptability, performance, efficiency, functionality and usability. However, cyber-physical systems are often safety critical, e.g., self-driving cars or medical devices, and the need for verification against potentially fatal accidents is of key importance.

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Bridge project

Secure Internet of Things – Risk Analysis in Design and Operation (SIoT)

This project aims to identify safety and security requirements for IoT systems and develop algorithms for quantitative risk assessment and decision-making. The aim is furthermore to create tools for designing and certifying IoT security training programs that will enable Danish companies to obtain security certification for their IoT devices, thus giving them a lead in a market that is likely to demand such certification in the near future. 

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Workstream manager

Aalborg University
Department of Computer Science

E: kgl@cs.aau.dk
T: +45 99 40 88 93

Contributing researchers

Alberto Lluch Lafuente

Associate Professor

Technical University of Denmark
DTU Compute

IT University of Copenhagen
Department of Computer Science

University of Copenhagen 
Department of Computer Science

University of Southern Denmark
Department of Mathematics and Computer Science

Jacob Nørbjerg

Associate Professor

Copenhagen Business School
Department of Digitalization

Aarhus University
Department of Computer Science

Hugo Andrés López

Associate Professor

Technical University of Denmark
DTU Compute

Andrey Rivkin

Assistant Professor

Tecnical University of Denmark
DTU Compute