Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach
Conference Proceeding
Christou, O., Pitropakis, N., Papadopoulos, P., Mckeown, S., & Buchanan, W. J. (2020)
Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach. In Proceedings of the 6th International Conference on Information Systems Security and Privacy. , (289-298). https://doi.org/10.5220/0008902202890298
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 ...
GLASS: Towards Secure and Decentralized eGovernance Services using IPFS
Conference Proceeding
Chrysoulas, C., Thomson, A., Pitropakis, N., Papadopoulos, P., Lo, O., Buchanan, W. J., …Tsolis, D. (2022)
GLASS: Towards Secure and Decentralized eGovernance Services using IPFS. In Computer Security. ESORICS 2021 International Workshops. https://doi.org/10.1007/978-3-030-95484-0_3
The continuously advancing digitization has provided answers to the bureaucratic problems faced by eGovernance services. This innovation led them to an era of automation, broa...
Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers
Conference Proceeding
Ali, H., Papadopoulos, P., Ahmad, J., Pit, N., Jaroucheh, Z., & Buchanan, W. J. (2022)
Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers. In IEEE SINCONF: 14th International Conference on Security of Information and Networks. https://doi.org/10.1109/SIN54109.2021.9699366
Threat information sharing is considered as one of the proactive defensive approaches for enhancing the overall security of trusted partners. Trusted partner organizations can...
A Distributed Trust Framework for Privacy-Preserving Machine Learning
Conference Proceeding
Abramson, W., Hall, A. J., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (2020)
A Distributed Trust Framework for Privacy-Preserving Machine Learning. In Trust, Privacy and Security in Digital Business. , (205-220). https://doi.org/10.1007/978-3-030-58986-8_14
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 ...
Min-max Training: Adversarially Robust Learning Models for Network Intrusion Detection Systems
Conference Proceeding
Grierson, S., Thomson, C., Papadopoulos, P., & Buchanan, B. (2022)
Min-max Training: Adversarially Robust Learning Models for Network Intrusion Detection Systems. In 2021 14th International Conference on Security of Information and Networks (SIN). https://doi.org/10.1109/sin54109.2021.9699157
Intrusion detection systems are integral to the security of networked systems for detecting malicious or anomalous network traffic. As traditional approaches are becoming less...