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Feature vulnerability and robustness assessment against adversarial machine learning attacks (2021)
Conference Proceeding
Mccarthy, A., Andriotis, P., Ghadafi, E., & Legg, P. (in press). Feature vulnerability and robustness assessment against adversarial machine learning attacks. In 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)https://doi.org/10.1109/CyberSA52016.2021.9478199

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 adver-sarial attacks. Given a trained classifier, a malicious attack may attempt to cra... Read More about Feature vulnerability and robustness assessment against adversarial machine learning attacks.

Unsupervised one-class learning for anomaly detection on home IoT network devices (2021)
Conference Proceeding
White, J., & Legg, P. (in press). Unsupervised one-class learning for anomaly detection on home IoT network devices. In 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)https://doi.org/10.1109/CyberSA52016.2021.9478248

In this paper we study anomaly detection methods for home IoT devices. Specifically, we address unsupervised one-class learning methods due to their ability to learn deviations from a single normal class. In a home IoT environment, this consideration... Read More about Unsupervised one-class learning for anomaly detection on home IoT network devices.

"Hacking an IoT Home": New opportunities for cyber security education combining remote learning with cyber-physical systems (2021)
Conference Proceeding
Legg, P., Higgs, T., Spruhan, P., White, J., & Johnson, I. (in press). "Hacking an IoT Home": New opportunities for cyber security education combining remote learning with cyber-physical systems. In 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA)https://doi.org/10.1109/CyberSA52016.2021.9478251

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.

Deep learning-based security behaviour analysis in IoT environments: A survey (2021)
Journal Article
Yue, Y., Li, S., Legg, P., & Li, F. (2021). Deep learning-based security behaviour analysis in IoT environments: A survey. Security and Communication Networks, 2021, 1-13. https://doi.org/10.1155/2021/8873195

Internet of Things (IoT) applications have been used in a wide variety of domains ranging from smart home, healthcare, smart energy, and Industrial 4.0. While IoT brings a number of benefits including convenience and efficiency, it also introduces a... Read More about Deep learning-based security behaviour analysis in IoT environments: A survey.

The visual design of network data to enhance cyber security awareness of the everyday internet user (2020)
Presentation / Conference
Carroll, F., Legg, P., & B√łnkel, B. (2020, June). The visual design of network data to enhance cyber security awareness of the everyday internet user. Paper presented at IEEE International Conference on Cyber Situational Awareness, Data Analytics and Assessment (Cyber Science 2020)

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
Wilkinson, G., & Legg, P. (2020, June). "What did you say?": Extracting unintentional secrets from predictive text learning systems. Paper presented at IEEE International Conference on Cyber Security and Protection of Digital Services (Cyber Science 2020)

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.

Shouting through letterboxes: A study on attack susceptibility of voice assistants (2020)
Presentation / Conference
Mccarthy, A., Gaster, B., & Legg, P. (2020, June). Shouting through letterboxes: A study on attack susceptibility of voice assistants. Paper presented at IEEE International Conference on Cyber Security and the Protection of Digital Services (Cyber Science 2020)

Voice assistants such as Amazon Echo and Google Home have become increasingly popular for many home users, for home automation, entertainment, and convenience. These devices process speech commands from a user to execute some action, such as playing... Read More about Shouting through letterboxes: A study on attack susceptibility of voice assistants.

Tools and techniques for improving cyber situational awareness of targeted phishing attacks (2019)
Conference Proceeding
Legg, P., & Blackman, T. (2019). Tools and techniques for improving cyber situational awareness of targeted phishing attacks. https://doi.org/10.1109/CyberSA.2019.8899406

© 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.

Efficient and interpretable real-time malware detection using random-forest (2019)
Conference Proceeding
Mills, A., Spyridopoulos, T., & Legg, P. (2019). Efficient and interpretable real-time malware detection using random-forest. https://doi.org/10.1109/CyberSA.2019.8899533

© 2019 IEEE. Malicious software, often described as malware, is one of the greatest threats to modern computer systems, and attackers continue to develop more sophisticated methods to access and compromise data and resources. Machine learning methods... Read More about Efficient and interpretable real-time malware detection using random-forest.

What makes for effective visualisation in cyber situational awareness for non-expert users? (2019)
Conference Proceeding
Carroll, F., Chakof, A., & Legg, P. (2019). What makes for effective visualisation in cyber situational awareness for non-expert users?. https://doi.org/10.1109/CyberSA.2019.8899440

© 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?.

Venue2Vec: An efficient embedding model for fine-grained user location prediction in geo-social networks (2019)
Journal Article
Xu, S., Cao, J., Legg, P., Liu, B., & Li, S. (2020). Venue2Vec: An efficient embedding model for fine-grained user location prediction in geo-social networks. IEEE Systems Journal, 14(2), 1740-1751. https://doi.org/10.1109/JSYST.2019.2913080

Geo-Social Networks (GSN) significantly improve location-aware capability of services by offering geo-located content based on the huge volumes of data generated in the GSN. The problem of user location prediction based on user-generated data in GSN... Read More about Venue2Vec: An efficient embedding model for fine-grained user location prediction in geo-social networks.

Visual analytics for collaborative human-machine confidence in human-centric active learning tasks (2019)
Journal Article
Legg, P., Smith, J., & Downing, A. (2019). Visual analytics for collaborative human-machine confidence in human-centric active learning tasks. Human-Centric Computing and Information Sciences, 9, https://doi.org/10.1186/s13673-019-0167-8

Active machine learning is a human-centric paradigm that leverages a small labelled dataset to build an initial weak classifier, that can then be improved over time through human-machine collaboration. As new unlabelled samples are observed, the mach... Read More about Visual analytics for collaborative human-machine confidence in human-centric active learning tasks.

Predicting user confidence during visual decision making (2018)
Journal Article
Smith, J., Legg, P., Matovis, M., & Kinsey, K. (2018). Predicting user confidence during visual decision making. ACM Transactions on Interactive Intelligent Systems, 8(2), https://doi.org/10.1145/3185524

© 2018 ACM People are not infallible consistent “oracles”: their confidence in decision-making may vary significantly between tasks and over time. We have previously reported the benefits of using an interface and algorithms that explicitly captured... Read More about Predicting user confidence during visual decision making.

Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models (2017)
Book Chapter
Smith, E. M., Smith, J., Legg, P., & Francis, S. (2017). Predicting the occurrence of world news events using recurrent neural networks and auto-regressive moving average models. In F. Chao, S. Schockaert, & Q. Zhang (Eds.), Advances in Computational Intelligence Systems: UKCI 2017, 191-202. Springer Cham

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.

RicherPicture: Semi-automated cyber defence using context-aware data analytics (2017)
Presentation / Conference
Erola, A., Agrafiotis, I., Happa, J., Goldsmith, M., Creese, S., & Legg, P. (2017, June). RicherPicture: Semi-automated cyber defence using context-aware data analytics. Paper presented at International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA 2017), London

In a continually evolving cyber-threat landscape, the detection and prevention of cyber attacks has become a complex task. Technological developments have led organisations to digitise the majority of their operations. This practice, however, has its... Read More about RicherPicture: Semi-automated cyber defence using context-aware data analytics.

Glyph visualization: A fail-safe design scheme based on quasi-hamming distances (2017)
Journal Article
Legg, P. A., Legg, P., Maguire, E., Walton, S., & Chen, M. (2017). Glyph visualization: A fail-safe design scheme based on quasi-hamming distances. IEEE Computer Graphics and Applications, 37(2), 31-41. https://doi.org/10.1109/MCG.2016.66

© 1981-2012 IEEE. In many spatial and temporal visualization applications, glyphs provide an effective means for encoding multivariate data. However, because glyphs are typically small, they are vulnerable to various perceptual errors. This article i... Read More about Glyph visualization: A fail-safe design scheme based on quasi-hamming distances.

Visual analytics for non-expert users in cyber situation awareness (2016)
Journal Article
Legg, P. (2016). Visual analytics for non-expert users in cyber situation awareness. https://doi.org/10.22619/IJCSA

Situation awareness is often described as the perception and comprehension of the current situation, and the projection of future status. Whilst this may be well understood in an organisational cybersecurity context, there is a strong case to be made... Read More about Visual analytics for non-expert users in cyber situation awareness.

Enhancing cyber situation awareness for non-expert users using visual analytics (2016)
Presentation / Conference
Legg, P. (2016, June). Enhancing cyber situation awareness for non-expert users using visual analytics. Paper presented at International Conference On Cyber Situational Awareness, Data Analytics And Assessment (CyberSA 2016)

Situation awareness is often described as the perception and comprehension of the current situation, and the projection of future status. Whilst this may be understood in an organisational cybersecurity context, there is a strong case to be made for... Read More about Enhancing cyber situation awareness for non-expert users using visual analytics.

Visualizing the insider threat: Challenges and tools for identifying malicious user activity (2015)
Presentation / Conference
Legg, P. (2015, October). Visualizing the insider threat: Challenges and tools for identifying malicious user activity. Paper presented at IEEE Symposium on Visualization for Cyber Security

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.