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Outputs (56)

Digital twins of cyber physical systems in smart manufacturing for threat simulation and detection with deep learning for time series classification (2024)
Presentation / Conference Contribution

With increasing reliance on Cyber Physical Systems (CPS) for automation and control in Industry 4.0 and 5.0, ensuring their security against cyber threats has become paramount. Traditional security mechanisms, constrained by operational continuity an... Read More about Digital twins of cyber physical systems in smart manufacturing for threat simulation and detection with deep learning for time series classification.

TRIST: Towards a container-based ICS testbed for cyber threat simulation and anomaly detection (2024)
Presentation / Conference Contribution

Cyber-attacks on Industrial Control Systems (ICS), as exemplified by the incidents at the Maroochy water treatment plant and the Ukraine's electric power grid, have demonstrated that cyber threats can inflict significant physical impacts. These incid... Read More about TRIST: Towards a container-based ICS testbed for cyber threat simulation and anomaly detection.

Cyber Funfair: Creating immersive and educational experiences for teaching Cyber Physical Systems Security (2024)
Presentation / Conference Contribution

Delivering meaningful and inspiring cyber security education for younger audiences can often be a challenge due to limited expertise and resources. Key to any outreach activity is that it both develops a learner's curiosity, as well as providing educ... Read More about Cyber Funfair: Creating immersive and educational experiences for teaching Cyber Physical Systems Security.

Evaluating data distribution strategies in federated learning: A trade-off analysis between privacy and performance for IoT security (2023)
Presentation / Conference Contribution

Federated learning is an effective approach for training a global machine learning model. It uses locally acquired data without having to share local data with the centralised server. This method provides a machine learning model beneficial for all p... Read More about Evaluating data distribution strategies in federated learning: A trade-off analysis between privacy and performance for IoT security.

Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification (2022)
Journal Article

Machine learning is key for automated detection of malicious network activity to ensure that computer networks and organizations are protected against cyber security attacks. Recently, there has been growing interest in the domain of adversarial mach... Read More about Defending against adversarial machine learning attacks using hierarchical learning: A case study on network traffic attack classification.

Teaching offensive and defensive cyber security in schools using a Raspberry Pi Cyber Range (2022)
Presentation / Conference Contribution

Computer Science as a subject is now appearing in more school curricula for GCSE and A level, with a growing demand for cyber security to be embedded within this teaching. Yet, teachers face challenges with limited time and resource for preparing pra... Read More about Teaching offensive and defensive cyber security in schools using a Raspberry Pi Cyber Range.

Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey (2022)
Journal Article

Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introdu... Read More about Functionality-preserving adversarial machine learning for robust classification in cybersecurity and intrusion detection domains: A survey.

Feature vulnerability and robustness assessment against adversarial machine learning attacks (2021)
Presentation / Conference Contribution

Whilst machine learning has been widely adopted for various domains, it is important to consider how such techniques may be susceptible to malicious users through adversarial attacks. Given a trained classifier, a malicious attack may attempt to craf... Read More about Feature vulnerability and robustness assessment against adversarial machine learning attacks.

"Hacking an IoT Home": New opportunities for cyber security education combining remote learning with cyber-physical systems (2021)
Presentation / Conference Contribution

In March 2020, the COVID-19 pandemic led to a dramatic shift in educational practice, whereby home-schooling and remote working became the norm. Many typical schools outreach projects to encourage uptake of learning cyber security skills therefore we... Read More about "Hacking an IoT Home": New opportunities for cyber security education combining remote learning with cyber-physical systems.

The visual design of network data to enhance cyber security awareness of the everyday internet user (2020)
Presentation / Conference Contribution

Technology and the use of online services are very prevalent across much of our everyday lives. As our digital interactions continue to grow, there is a need to improve public awareness of the risks to our personal online privacy and security. Design... Read More about The visual design of network data to enhance cyber security awareness of the everyday internet user.

"What did you say?": Extracting unintentional secrets from predictive text learning systems (2020)
Presentation / Conference Contribution

As a primary form of communication, text is used widely in applications including e-mail conversations, mobile text messaging, chatrooms, and forum discussions. Modern systems include facilities such as predictive text, recently implemented using dee... Read More about "What did you say?": Extracting unintentional secrets from predictive text learning systems.

What makes for effective visualisation in cyber situational awareness for non-expert users? (2019)
Presentation / Conference Contribution

© 2019 IEEE. As cyber threats continue to become more prevalent, there is a need to consider how best we can understand the cyber landscape when acting online, especially so for non-expert users. Satellite navigation systems provide the de facto stan... Read More about What makes for effective visualisation in cyber situational awareness for non-expert users?.

Tools and techniques for improving cyber situational awareness of targeted phishing attacks (2019)
Presentation / Conference Contribution

© 2019 IEEE. Phishing attacks continue to be one of the most common attack vectors used online today to deceive users, such that attackers can obtain unauthorised access or steal sensitive information. Phishing campaigns often vary in their level of... Read More about Tools and techniques for improving cyber situational awareness of targeted phishing attacks.

Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models (2017)
Book Chapter

The ability to predict future states is fundamental for a wide variety of applications, from weather forecasting to stock market analysis. Understanding the related data attributes that can influence changes in time series is a challenging task that... Read More about Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models.

Visualizing the insider threat: Challenges and tools for identifying malicious user activity (2015)
Presentation / Conference Contribution

One of the greatest challenges for managing organisational cyber security is the threat that comes from those who operate within the organisation. With entitled access and knowledge of organisational processes, insiders who choose to attack have the... Read More about Visualizing the insider threat: Challenges and tools for identifying malicious user activity.

Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging (2014)
Journal Article

© 2014 Elsevier Ltd. All rights reserved. Multi-modal image registration is becoming an increasingly powerful tool for medical diagnosis and treatment. The combination of different image modalities facilitates much greater understanding of the underl... Read More about Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging.

Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation (2013)
Journal Article

Mutual information (MI) is a popular similarity measure for performing image registration between different modalities. MI makes a statistical comparison between two images by computing the entropy from the probability distribution of the data. There... Read More about Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation.

A robust solution to multi-modal image registration by combining mutual information with multi-scale derivatives (2009)
Presentation / Conference Contribution

In this paper we present a novel method for performing image registration of different modalities. Mutual Information (MI) is an established method for performing such registration. However, it is recognised that standard MI is not without some probl... Read More about A robust solution to multi-modal image registration by combining mutual information with multi-scale derivatives.

Incorporating neighbourhood feature derivatives with Mutual Information to improve accuracy of multi-modal image registration
Presentation / Conference Contribution

In this paper we present an improved method for performing image registration of different modalities. Russakoff [1] proposed the method of Regional Mutual Information (RMI) which allows neighbourhood information to be considered in the Mutual Inform... Read More about Incorporating neighbourhood feature derivatives with Mutual Information to improve accuracy of multi-modal image registration.

Improving accuracy and efficiency of registration by mutual information using Sturges’ Histogram Rule
Presentation / Conference Contribution

Mutual Information is a common technique for image registration in the medical domain, in particular where images of different modalities are to be registered. In this paper, we wish to demonstrate the benefits of applying a common method known in st... Read More about Improving accuracy and efficiency of registration by mutual information using Sturges’ Histogram Rule.