Outputs (9)

Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers (2022)
Presentation / Conference Contribution
Ali, H., Papadopoulos, P., Ahmad, J., Pit, N., Jaroucheh, Z., & Buchanan, W. J. (2021, December). Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers. Presented at IEEE SINCONF: 14th International Conference on Security of

Threat information sharing is considered as one of the proactive defensive approaches for enhancing the overall security of trusted partners. Trusted partner organizations can provide access to past and current cybersecurity threats for reducing the... Read More about Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers.

Min-max Training: Adversarially Robust Learning Models for Network Intrusion Detection Systems (2022)
Presentation / Conference Contribution
Grierson, S., Thomson, C., Papadopoulos, P., & Buchanan, B. (2021, December). Min-max Training: Adversarially Robust Learning Models for Network Intrusion Detection Systems. Presented at 2021 14th International Conference on Security of Information and Ne

Intrusion detection systems are integral to the security of networked systems for detecting malicious or anomalous network traffic. As traditional approaches are becoming less effective, machine learning and deep learning-based intrusion detection sy... Read More about Min-max Training: Adversarially Robust Learning Models for Network Intrusion Detection Systems.

GLASS: Towards Secure and Decentralized eGovernance Services using IPFS (2022)
Presentation / Conference Contribution
Chrysoulas, C., Thomson, A., Pitropakis, N., Papadopoulos, P., Lo, O., Buchanan, W. J., Domalis, G., Karacapilidis, N., Tsakalidis, D., & Tsolis, D. (2021, October). GLASS: Towards Secure and Decentralized eGovernance Services using IPFS. Presented at 7th

The continuously advancing digitization has provided answers to the bureaucratic problems faced by eGovernance services. This innovation led them to an era of automation, broadened the attack surface and made them a popular target for cyber attacks.... Read More about GLASS: Towards Secure and Decentralized eGovernance Services using IPFS.

Evaluating Tooling and Methodology when Analysing Bitcoin Mixing Services After Forensic Seizure (2021)
Presentation / Conference Contribution
Young, E. H., Chrysoulas, C., Pitropakis, N., Papadopoulos, P., & Buchanan, W. J. (2021, October). Evaluating Tooling and Methodology when Analysing Bitcoin Mixing Services After Forensic Seizure. Paper presented at International Conference on Data Analyt

Little or no research has been directed to analysis and researching forensic analysis of the Bitcoin mixing or 'tumbling' service themselves. This work is intended to examine effective tooling and methodology for recovering forensic artifacts from tw... Read More about Evaluating Tooling and Methodology when Analysing Bitcoin Mixing Services After Forensic Seizure.

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., …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

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.

Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach (2020)
Presentation / Conference Contribution
Christou, O., Pitropakis, N., Papadopoulos, P., Mckeown, S., & Buchanan, W. J. (2020, February). Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach. Presented at ICISSP 2020, Valletta, Malta

Phishing is considered to be one of the most prevalent cyber-attacks because of its immense flexibility and alarmingly high success rate. Even with adequate training and high situational awareness, it can still be hard for users to continually be awa... Read More about Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach.

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., …Titcombe, T. (2020, December). Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning. Poster presented at Neu

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 Distributed Trust Framework for Privacy-Preserving Machine Learning (2020)
Presentation / Conference Contribution
Abramson, W., Hall, A. J., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2020, September). A Distributed Trust Framework for Privacy-Preserving Machine Learning. Presented at The 17th International Conference on Trust, Privacy and Security in Digit

When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust between data owners and data scientists. Data owners are justifiably reluct... Read More about A Distributed Trust Framework for Privacy-Preserving Machine Learning.