All Outputs (5)

Min-max Training: Adversarially Robust Learning Models for Network Intrusion Detection Systems (2022)
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 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.

Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers (2022)
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 provide access to past and current cybersecurity threats for reducing the... Read More about Privacy-preserving and Trusted Threat Intelligence Sharing using Distributed Ledgers.

GLASS: Towards Secure and Decentralized eGovernance Services using IPFS (2022)
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, 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.

Phishing URL Detection Through Top-Level Domain Analysis: A Descriptive Approach (2020)
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 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.

A Distributed Trust Framework for Privacy-Preserving Machine Learning (2020)
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 between data owners and data scientists. Data owners are justifiably reluct... Read More about A Distributed Trust Framework for Privacy-Preserving Machine Learning.