Outputs (1696)

Green Building: An antidote to sick buliding syndrome menace in Africa (2023)
Conference Proceeding
Liphadzi, M., Osunsanmi, T., Aigbavboa, C. O., Thwala, W., Phuti, T., & Aliu, J. (2023). Green Building: An antidote to sick buliding syndrome menace in Africa. In Proceedings of the Creative Construction Conference 2023 (632-644). https://doi.org/10.3311/ccc2023-082

Sick building syndrome (SBS) is the leading cause of the reduction in the building's occupancy level of satisfaction, poor indoor air quality, and other shenanigans responsible for the underperformance of building occupants and loss in property value... Read More about Green Building: An antidote to sick buliding syndrome menace in Africa.

A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs (2023)
Conference Proceeding
McLaren, R. A., Babaagba, K., & Tan, Z. (2023). A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs. In Machine Learning, Optimization, and Data Science: 8th International Conference, LOD 2022, Certosa di Pontignano, Italy, September 19–22, 2022, Revised Selected Papers, Part II (32-46). https://doi.org/10.1007/978-3-031-25891-6_4

As the field of malware detection continues to grow, a shift in focus is occurring from feature vectors and other common, but easily obfuscated elements to a semantics based approach. This is due to the emergence of more complex malware families that... Read More about A Generative Adversarial Network Based Approach to Malware Generation Based on Behavioural Graphs.

Practical Cyber Threat Intelligence in the UK Energy Sector (2023)
Conference Proceeding
Paice, A., & McKeown, S. (2023). Practical Cyber Threat Intelligence in the UK Energy Sector. In Proceedings of the International Conference on Cybersecurity, Situational Awareness and Social Media (3-23). https://doi.org/10.1007/978-981-19-6414-5_1

The UK energy sector is a prime target for cyber-attacks by foreign states, criminals, ‘hacktivist’ groups, and terrorists. As Critical National Infrastructure (CNI), the industry needs to understand the threats it faces to mitigate risks and make ef... Read More about Practical Cyber Threat Intelligence in the UK Energy Sector.

Neutralising Adversarial Machine Learning in Industrial Control Systems Using Blockchain (2023)
Conference Proceeding
Moradpoor, N., Barati, M., Robles-Durazno, A., Abah, E., & McWhinnie, J. (2023). Neutralising Adversarial Machine Learning in Industrial Control Systems Using Blockchain. In Proceedings of the International Conference on Cybersecurity, Situational Awareness and Social Media (437-451). https://doi.org/10.1007/978-981-19-6414-5_24

The protection of critical national infrastructures such as drinking water, gas, and electricity is extremely important as nations are dependent on their operation and steadiness. However, despite the value of such utilities their security issues hav... Read More about Neutralising Adversarial Machine Learning in Industrial Control Systems Using Blockchain.

Developing a Digital ForensicsTerminology Using Natural Language Processing (2022)
Conference Proceeding
Le Gall, M., Cole, C., Haynes, D., & Nic Daeid, N. (2022). Developing a Digital ForensicsTerminology Using Natural Language Processing. In M. Lykke, T. Svarre, D. Haynes, M. Skov, M. Thellesfsen, & D. Martinez-Avila (Eds.), Knowledge Organization across Disciplines, Domains, Services and Technologies: Proceedings of the Seventeenth International ISKO Conference (173-186)

Deep Pre-trained Contrastive Self-Supervised Learning: A Cyberbullying Detection Approach with Augmented Datasets (2022)
Conference Proceeding
Alharigy, L. M., Al-Nuaim, H. A., & Moradpoor, N. (2022). Deep Pre-trained Contrastive Self-Supervised Learning: A Cyberbullying Detection Approach with Augmented Datasets. In 2022 14th International Conference on Computational Intelligence and Communication Networks (CICN). https://doi.org/10.1109/CICN56167.2022.10008274

Cyberbullying is a widespread problem that has only increased in recent years due to the massive dependence on social media. Although, there are many approaches for detecting cyberbullying they still need to be improved upon for more accurate detecti... Read More about Deep Pre-trained Contrastive Self-Supervised Learning: A Cyberbullying Detection Approach with Augmented Datasets.

Post Quantum Cryptography Analysis of TLS Tunneling on a Constrained Device (2022)
Conference Proceeding
Barton, J., Pitropakis, N., Buchanan, W., Sayeed, S., & Abramson, W. (2022). Post Quantum Cryptography Analysis of TLS Tunneling on a Constrained Device. In Proceedings of the 8th International Conference on Information Systems Security and Privacy - ICISSP (551-561). https://doi.org/10.5220/0010903000003120

Advances in quantum computing make Shor’s algorithm for factorising numbers ever more tractable. This threatens the security of any cryptographic system which often relies on the difficulty of factorisation. It also threatens methods based on discret... Read More about Post Quantum Cryptography Analysis of TLS Tunneling on a Constrained Device.

High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network (2022)
Conference Proceeding
Zhang, Z., Li, Y., Liu, Q., & Liu, X. (2022). High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network. In 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). https://doi.org/10.1109/dasc/picom/cbdcom/cy55231.2022.9927756

A basic stage of hydrological research is to automatically extract water body information from high-resolution remote sensing images. Various methods based on deep learning convolutional neural networks have been proposed in recent studies to achieve... Read More about High Resolution Remote Sensing Water Image Segmentation Based on Dual Branch Network.

Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract) (2022)
Conference Proceeding
Sampath Kalutharage, C., Liu, X., & Chrysoulas, C. (2022). Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract). In Attacks and Defenses for the Internet-of-Things: 5th International Workshop, ADIoT 2022 (41-50). https://doi.org/10.1007/978-3-031-21311-3_8

Over the past few decades, Machine Learning (ML)-based intrusion detection systems (IDS) have become increasingly popular and continue to show remarkable performance in detecting attacks. However, the lack of transparency in their decision-making pro... Read More about Explainable AI and Deep Autoencoders Based Security Framework for IoT Network Attack Certainty (Extended Abstract).

Multi-Agent Modelling Notation (MAMN): A multi-layered graphical modelling notation for agent-based simulations (2022)
Conference Proceeding
Nguyen, J., Powers, S., Urquhart, N., Farrenkopf, T., & Guckert, M. (2022). Multi-Agent Modelling Notation (MAMN): A multi-layered graphical modelling notation for agent-based simulations. In PRIMA 2022: Principles and Practice of Multi-Agent Systems - 24th International Conference, Valencia, Spain, November 16–18, 2022, Proceedings (640-649). https://doi.org/10.1007/978-3-031-21203-1_42

Cause-effect graphs have been applied in non agent-based simulations, where they are used to model chained causal relations between input parameters and system behaviour measured by appropriate indicators. This can be useful for the analysis and inte... Read More about Multi-Agent Modelling Notation (MAMN): A multi-layered graphical modelling notation for agent-based simulations.