Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT
Journal Article
Papadopoulos, P., Thornewill Von Essen, O., Pitropakis, N., Chrysoulas, C., Mylonas, A., & Buchanan, W. J. (2021)
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT. Journal of Cybersecurity and Privacy, 1(2), 252-273. https://doi.org/10.3390/jcp1020014
As the internet continues to be populated with new devices and emerging technologies, the attack surface grows exponentially. Technology is shifting towards a profit-driven In...
A Comparative Analysis of Honeypots on Different Cloud Platforms
Journal Article
Kelly, C., Pitropakis, N., Mylonas, A., McKeown, S., & Buchanan, W. J. (2021)
A Comparative Analysis of Honeypots on Different Cloud Platforms. Sensors, 21(7), https://doi.org/10.3390/s21072433
In 2019, the majority of companies used at least one cloud computing service and it is expected that by the end of 2021, cloud data centres will process 94% of workloads. The ...
Privacy and Trust Redefined in Federated Machine Learning
Journal Article
Papadopoulos, P., Abramson, W., Hall, A. J., Pitropakis, N., & Buchanan, W. J. (2021)
Privacy and Trust Redefined in Federated Machine Learning. Machine Learning and Knowledge Extraction, 3(2), 333-356. https://doi.org/10.3390/make3020017
A common privacy issue in traditional machine learning is that data needs to be disclosed for the training procedures. In situations with highly sensitive data such as healthc...
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 ...
Privacy-preserving Surveillance Methods using Homomorphic Encryption
Conference Proceeding
Bowditch, W., Abramson, W., Buchanan, W. J., Pitropakis, N., & Hall, A. J. (2020)
Privacy-preserving Surveillance Methods using Homomorphic Encryption. In ICISSP: Proceedings of the 6th International Conference on Information Systems Security and Privacy. , (240-248). https://doi.org/10.5220/0008864902400248
Data analysis and machine learning methods often involve the processing of cleartext data, and where this could breach the rights to privacy. Increasingly, we must use encrypt...
Review and Critical Analysis of Privacy-preserving Infection Tracking and Contact Tracing
Journal Article
Buchanan, W. J., Imran, M. A., Ur-Rehman, M., Zhang, L., Abbasi, Q. H., Chrysoulas, C., …Papadopoulos, P. (2020)
Review and Critical Analysis of Privacy-preserving Infection Tracking and Contact Tracing. Frontiers in Communications and Networks, https://doi.org/10.3389/frcmn.2020.583376
The outbreak of viruses have necessitated contact tracing and infection tracking methods. Despite various efforts, there is currently no standard scheme for the tracing and tr...
A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric
Journal Article
Stamatellis, C., Papadopoulos, P., Pitropakis, N., Katsikas, S., & Buchanan, W. J. (2020)
A Privacy-Preserving Healthcare Framework Using Hyperledger Fabric. Sensors, 20(22), https://doi.org/10.3390/s20226587
Electronic health record (EHR) management systems require the adoption of effective technologies when health information is being exchanged. Current management approaches ofte...
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 ...
Privacy-Preserving Passive DNS
Journal Article
Papadopoulos, P., Pitropakis, N., Buchanan, W. J., Lo, O., & Katsikas, S. (2020)
Privacy-Preserving Passive DNS. Computers, 9(3), https://doi.org/10.3390/computers9030064
The Domain Name System (DNS) was created to resolve the IP addresses of web servers to easily remembered names. When it was initially created, security was not a major concern...
Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier
Conference Proceeding
Hall, A. J., Pitropakis, N., Buchanan, W. J., & Moradpoor, N. (2019)
Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier. In 2018 IEEE International Conference on Big Data (Big Data)https://doi.org/10.1109/BigData.2018.8621922
Insider threats continue to present a major challenge for the information security community. Despite constant research taking place in this area; a substantial gap still exis...