Current Projects

ERASE: Evaluating the Robustness of Machine Learning Algorithms in Adversarial Settings

We are increasingly relying on systems that use machine learning to learn from their environment and often to detect anomalies in the behaviour that they observe. But the consequences of a malicious adversary targeting the machine learning algorithms themselves by compromising part of the data from which the system learns are poorly understood and represent a significant threat. The objective of this project is to propose systematic and realistic ways of assessing, testing and improving the robustness of machine learning algorithms to poisoning attacks. We consider both indiscriminate attacks, which aim to cause an overall degradation of the model’s performance, and targeted attacks that aim to induce specific errors. We focus in particular on “optimal” attack strategies seeking to maximise the impact of the poisoning points, thus representing a “worst-case” scenario. However, we consider sophisticated adversaries that also take into account detectability constraints.   

PhD Studentship funded by DSTL

Responding to Attacks and Compromise at the Edge (RACE)

IoT systems evolve dynamically and are increasingly used in critical applications. Understanding how to maintain the operation of the system when systems have been partially compromised is therefore of critical importance. This requires to continuously assess the risk to other parts of the system, determine the impact of the compromise and to select appropriate mitigation strategies to respond to the attack. The ability to cope with dynamic system changes is a key and significant challenge in achieving these objectives.

RACE is articulated into four broad themes of work: understanding attacks and mitigation strategies, maintaining an adequate representation of risk to the other parts of the system by understanding how attacks can evolve and propagate, understanding the impact of the compromise upon the functionality of the system and selecting countermeasure strategies taking into account trade-offs between minimising disruption to the system operation and functionality provided and minimising the risk to the other parts of the system.

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.

Project Introduction

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.