Current Projects

MUSKETEER: Machine learning to augment shared knowledge in federated privacy-preserving scenarios

The massive increase in data collected and stored worldwide calls for new ways to preserve privacy while still allowing data sharing among multiple data owners. Today, the lack of trusted and secure environments for data sharing inhibits data economy while legality, privacy, trustworthiness, data value and confidentiality hamper the free flow of data. By the end of the project, MUSKETEER aims to create a validated, federated, privacy-preserving machine learning platform tested on industrial data that is inter-operable, scalable and efficient enough to be deployed in real use cases. MUSKETEER aims to alleviate data sharing barriers by providing secure, scalable and privacy-preserving analytics over decentralized datasets using machine learning. Data can continue to be stored in different locations with different privacy constraints, but shared securely. The MUSKETEER cross-domain platform will validate progress in the industrial scenarios of smart manufacturing and health. MUSKETEER strives to (1) create machine learning models over a variety of privacy-preserving scenarios, (2) ensure security and robustness against external and internal threats, (3) provide a standardized and extendable architecture, (4) demonstrate and validate in two different industrial scenarios and (5) enhance data economy by boosting sharing across domains. The MUSKETEER impact crosses industrial, scientific, economic and strategic domains. Real-world industry requirements and outcomes are validated in an operational setting. Federated machine learning approaches for data sharing are innovated. Data economy is fostered by creating a rewarding model capable of fairly monetizing datasets according to the real data value. Finally, Europe is positioned as a leader in innovative data sharing technologies.

Visit Musketeer’s website here.

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AF-Cyber: Logic-based Attribution and Forensics in Cyber Security

Connected devices will continue to grow in volume and variety. The increase of connectivity brings a drastic impact on the increase of cyber attacks. Protecting measurements are not enough, while finding who did the attack is a crucial for preventing the escalation of cyber attacks. The impact of forensics in cyber security is becoming essential for the reduction and mitigation of attacks. Forensics and attribution forensics come along with their own challenges, like the difficulties on collecting suitable evidence, and the vastness of anti-forensics tools used by the attackers to cover their traces.

The main goal of AF-Cyber is to investigate and analyse the problem of attributing cyber attacks. We plan to construct a logic-based framework for performing attribution of cyber attacks, based on cyber forensics evidence, social science approaches and an intelligent methodology for dynamic evidence collection. AF-Cyber will relieve part of the cyberattacks problem, by supporting forensics investigation and attribution with logical-based frameworks representation, reasoning and supporting tools. AF-Cyber is multi-disciplinary and collaborative, bridging forensics in cyber attacks, theoretical computer science (logics and formal proofs), security, software engineering, and social science.

AF-Cyber received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 746667.

 

 

SECRIS: Security Risk Assessment of IoT Environments with Attack Graph Models

IoT environments are vulnerable: many devices can be accessed physically and are not designed with security in mind. It is often impractical to patch all the vulnerabilities or to eliminate all possible threats. Unlike more traditional computing systems IoT environments bring together the physical, human and cyber aspects of a system. Each can be used to compromise the other and each can contribute towards monitoring and protecting the other.

Given the complexity of possible attacks, techniques for identifying and assessing the security risk are needed. In traditional networked environments attack graphs have been proven as a powerful tool for representing the different paths through which a system can be compromised. In this project we propose to design a new generation of attack graph models capable of describing the attack surface of modern IoT infrastructures for smart buildings. We are investigating new mechanisms to reduce the complexity of the attack graph representations and efficient algorithms for their analysis.

PETRAS Hub for the IoT

PETRAS_logo_black smallWe are thrilled to be part of the PETRAS IoT Hub which aims to ensure that the UK remains a global leader in the Internet of Things. PETRAS (Privacy, Ethics, Trust, Reliability, Acceptability and Security) groups together 9 leading UK universities and has more than 47 user partners from industry and the public sector. The consortium has received a £9.8M grant from the EPSRC. Dr Emil Lupu is to serve as Deputy Director of the Hub and lead for the Security and Safety Theme.

CIPART: Cloud Intelligent Protection at Run-Time

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Organisations, small and large, increasingly rely upon cloud environments to supply their ICT needs because clouds provide a better incremental cost structure, resource elasticity and simpler management. This trend is set to continue as increasingly information collected from mobile devices and smart environments including homes, infrastructures and smart-cities is uploaded and processed in cloud environments. Services delivered to users are also deployed in the cloud as this provides better scaleability and in some cases permits migration closer to the point of access for reduced latency.

Clouds are therefore an attractive target for organised and skilled cyber-attacks. They are also more vulnerable as they host environments from multiple tenant organisations with different interests and different risk aversion profiles. Yet clouds also offer opportunities for better protection both pro-actively and reactively in response to a persistent attack. …