Outputs (408)

A Study of Online Safety and Digital Literacy of Academic Researchers Working from Home during the COVID-19 Pandemic (2022)
Presentation / Conference Contribution
Haynes, D., & Salzano, R. (2022, April). A Study of Online Safety and Digital Literacy of Academic Researchers Working from Home during the COVID-19 Pandemic. Paper presented at ASIS&T Global 24-hour Conference, 2022, Online

Universities in the UK responded to the COVID-19 pandemic by moving teaching to an online environment and requiring staff to work from home, as far as possible. Researchers face particular challenges of security and privacy where their work involves... Read More about A Study of Online Safety and Digital Literacy of Academic Researchers Working from Home during the COVID-19 Pandemic.

Evaluation of Live Forensic Techniques in Ransomware Attack Mitigation (2020)
Thesis
Davies, S. (2020). Evaluation of Live Forensic Techniques in Ransomware Attack Mitigation. (Dissertation). Edinburgh Napier University. Retrieved from http://researchrepository.napier.ac.uk/Output/2875361

Ransomware continues to grow in both scale, cost, complexity and impact since its initial discovery nearly 30 years ago. Security practitioners are engaged in a continual "arms race" with the ransomware developers attempting to defend their digital i... Read More about Evaluation of Live Forensic Techniques in Ransomware Attack Mitigation.

Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review (2022)
Journal Article
Gulzar Ahmad, S., Iqbal, T., Javaid, A., Ullah Munir, E., Kirn, N., Jan, S. U., & Ramzan, N. (2022). Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review. Sensors, 22(12), Article 4362. https://doi.org/10.3390/s22124362

Currently, information and communication technology (ICT) allows health institutions to reach disadvantaged groups in rural areas using sensing and artificial intelligence (AI) technologies. Applications of these technologies are even more essential... Read More about Sensing and Artificial Intelligent Maternal-Infant Health Care Systems: A Review.

Ensemble learning-based IDS for sensors telemetry data in IoT networks (2022)
Journal Article
Naz, N., Khan, M. A., Alsuhibany, S. A., Diyan, M., Tan, Z., Khan, M. A., & Ahmad, J. (2022). Ensemble learning-based IDS for sensors telemetry data in IoT networks. Mathematical Biosciences and Engineering, 19(10), 10550-10580. https://doi.org/10.3934/mbe.2022493

The Internet of Things (IoT) is a paradigm that connects a range of physical smart devices to provide ubiquitous services to individuals and automate their daily tasks. IoT devices collect data from the surrounding environment and communicate with ot... Read More about Ensemble learning-based IDS for sensors telemetry data in IoT networks.

Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform (2022)
Journal Article
Ali, H., Ahmad, J., Jaroucheh, Z., Papadopoulos, P., Pitropakis, N., Lo, O., Abramson, W., & Buchanan, W. J. (2022). Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform. Entropy, 24(10), Article 1379. https://doi.org/10.3390/e24101379

Historically, threat information sharing has relied on manual modelling and centralised network systems, which can be inefficient, insecure, and prone to errors. Alternatively, private blockchains are now widely used to address these issues and impro... Read More about Trusted Threat Intelligence Sharing in Practice and Performance Benchmarking through the Hyperledger Fabric Platform.

Comparison of Entropy Calculation Methods for Ransomware Encrypted File Identification (2022)
Journal Article
Davies, S. R., Macfarlane, R., & Buchanan, W. J. (2022). Comparison of Entropy Calculation Methods for Ransomware Encrypted File Identification. Entropy, 24(10), Article 1503. https://doi.org/10.3390/e24101503

Ransomware is a malicious class of software that utilises encryption to implement an attack on system availability. The target’s data remains encrypted and is held captive by the attacker until a ransom demand is met. A common approach used by many c... Read More about Comparison of Entropy Calculation Methods for Ransomware Encrypted File Identification.

PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN (2021)
Presentation / Conference Contribution
Romanini, D., Hall, A. J., Papadopoulos, P., Titcombe, T., Ismail, A., Cebere, T., Sandmann, R., Roehm, R., & Hoeh, M. A. (2021, May). PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN. Poster presented at ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML 2021), Online

We introduce PyVertical, a framework supporting vertical federated learning using split neural networks. The proposed framework allows a data scientist to train neural networks on data features vertically partitioned across multiple owners while keep... Read More about PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN.

Practical defences against model inversion attacks for split neural networks (2021)
Presentation / Conference Contribution
Titcombe, T., Hall, A. J., Papadopoulos, P., & Romanini, D. (2021, May). Practical defences against model inversion attacks for split neural networks. Paper presented at ICLR 2021 Workshop on Distributed and Private Machine Learning (DPML 2021), Online

We describe a threat model under which a split network-based federated learning system is susceptible to a model inversion attack by a malicious computational server. We demonstrate that the attack can be successfully performed with limited knowledge... Read More about Practical defences against model inversion attacks for split neural networks.

Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning (2020)
Presentation / Conference Contribution
Angelou, N., Benaissa, A., Cebere, B., Clark, W., Hall, A. J., Hoeh, M. A., Liu, D., Papadopoulos, P., Roehm, R., Sandmann, R., Schoppmann, P., & Titcombe, T. (2020, December). Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning. Poster presented at NeurIPS 2020 Workshop on Privacy Preserving Machine Learning (PPML 2020), Online

We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-based PSI and PSI-C protocols with compression based on Bloom filters th... Read More about Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning.

A framework for live host-based Bitcoin wallet forensics and triage (2022)
Journal Article
Holmes, A., & Buchanan, W. J. (2023). A framework for live host-based Bitcoin wallet forensics and triage. Forensic Science International: Digital Investigation, 44, Article 301486. https://doi.org/10.1016/j.fsidi.2022.301486

Organised crime and cybercriminals use Bitcoin, a popular cryptocurrency, to launder money and move it across borders with impunity. The UK and other countries have legislation to recover the proceeds of crime from criminals. Recent UK case law has r... Read More about A framework for live host-based Bitcoin wallet forensics and triage.

Using Social Media & Sentiment Analysis to Make Investment Decisions (2022)
Journal Article
Hasselgren, B., Chrysoulas, C., Pitropakis, N., & Buchanan, W. J. (2023). Using Social Media & Sentiment Analysis to Make Investment Decisions. Future Internet, 15(1), Article 5. https://doi.org/10.3390/fi15010005

Making investment decisions by utilizing sentiment data from social media (SM) is starting to become a more tangible concept. There has been a broad investigation into this field of study over the last decade, and many of the findings have promising... Read More about Using Social Media & Sentiment Analysis to Make Investment Decisions.

Towards The Creation Of The Future Fish Farm (2023)
Journal Article
Papadopoulos, P., Buchanan, W. J., Sayeed, S., & Pitropakis, N. (2023). Towards The Creation Of The Future Fish Farm. Journal of Surveillance, Security and Safety, 4, 1-3. https://doi.org/10.20517/jsss.2022.16

Aim: A fish farm is an area where fish raise and bred for food. Fish farm environments support the care and management of seafood within a controlled environment. Over the past few decades, there has been a remarkable increase in the calorie intake o... Read More about Towards The Creation Of The Future Fish Farm.

An omnidirectional approach to touch-based continuous authentication (2023)
Journal Article
Aaby, P., Giuffrida, M. V., Buchanan, W. J., & Tan, Z. (2023). An omnidirectional approach to touch-based continuous authentication. Computers and Security, 128, Article 103146. https://doi.org/10.1016/j.cose.2023.103146

This paper focuses on how touch interactions on smartphones can provide a continuous user authentication service through behaviour captured by a touchscreen. While efforts are made to advance touch-based behavioural authentication, researchers often... Read More about An omnidirectional approach to touch-based continuous authentication.

A Stacking Ensemble of Deep Learning Models for IoT Network Intrusion Detection (2023)
Preprint / Working Paper
Lazzarini, R., Tianfield, H., & Charissis, V. A Stacking Ensemble of Deep Learning Models for IoT Network Intrusion Detection

The number of Internet of Things (IoT) devices has increased considerably inthe past few years, which resulted in an exponential growth of cyber attackson IoT infrastructure. As a consequence, the prompt detection of attacks inIoT environments throug... Read More about A Stacking Ensemble of Deep Learning Models for IoT Network Intrusion Detection.

A Blockchain-based two Factor Honeytoken Authentication System (2023)
Presentation / Conference Contribution
Papaspirou, V., Maglaras, L., Kantzavelou, I., Moradpoor, N., & Katsikas, S. (2023, September). A Blockchain-based two Factor Honeytoken Authentication System. Poster presented at 28th European Symposium on Research in Computer Security (ESORICS), The Hague

This paper extends and advances our recently introduced two-factor Honeytoken authentication method by incorporating blockchain technology. This novel approach strengthens the authentication method, preventing various attacks, including tampering att... Read More about A Blockchain-based two Factor Honeytoken Authentication System.

Federated Learning for IoT Intrusion Detection (2023)
Journal Article
Lazzarini, R., Tianfield, H., & Charissis, V. (2023). Federated Learning for IoT Intrusion Detection. Artificial Intelligence, 4(3), 509-530. https://doi.org/10.3390/ai4030028

The number of Internet of Things (IoT) devices has increased considerably in the past few years, resulting in a large growth of cyber attacks on IoT infrastructure. As part of a defense in depth approach to cybersecurity, intrusion detection systems... Read More about Federated Learning for IoT Intrusion Detection.

Start thinking in graphs: using graphs to address critical attack paths in a Microsoft cloud tenant (2023)
Journal Article
Elmiger, M., Lemoudden, M., Pitropakis, N., & Buchanan, W. J. (2024). Start thinking in graphs: using graphs to address critical attack paths in a Microsoft cloud tenant. International Journal of Information Security, 23, 467-485. https://doi.org/10.1007/s10207-023-00751-6

The challenge of securing IT environments has reached a new complexity level as a growing number of organisations adopt cloud solutions. This trend increases the possibility of overseen attack paths in an organisation’s IT infrastructure. This paper... Read More about Start thinking in graphs: using graphs to address critical attack paths in a Microsoft cloud tenant.

A stacking ensemble of deep learning models for IoT intrusion detection (2023)
Journal Article
Lazzarini, R., Tianfield, H., & Charissis, V. (2023). A stacking ensemble of deep learning models for IoT intrusion detection. Knowledge-Based Systems, 279, Article 110941. https://doi.org/10.1016/j.knosys.2023.110941

The number of Internet of Things (IoT) devices has increased considerably in the past few years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a consequence, the prompt detection of attacks in IoT environments thr... Read More about A stacking ensemble of deep learning models for IoT intrusion detection.

Use and operational safety (2023)
Book Chapter
Reed, N., Charisis, V., & Cowper, S. (2023). Use and operational safety. In D. Ventriglia, & M. Kahl (Eds.), FISITA Intelligent Safety White Paper – The Safety of Electro-Mobility: Expert considerations on the Safety of an Electric Vehicle from concept through end of life (107-111). FISITA

Whether you are an individual buying your first car or replacing an existing vehicle, or if you are a fleet manager making vehicle purchase decisions on behalf of a company, the acquisition of a car is usually a highly significant purchase. Increasi... Read More about Use and operational safety.

Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection (2023)
Journal Article
Manikandaraja, A., Aaby, P., & Pitropakis, N. (2023). Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection. Computers, 12(10), Article 195. https://doi.org/10.3390/computers12100195

Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices. Because machine learning and artificial intelligence can detect malware better than traditional... Read More about Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection.