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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. 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...

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. 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 ...

Testing And Hardening IoT Devices Against the Mirai Botnet

Conference Proceeding
Kelly, C., Pitropakis, N., McKeown, S., & Lambrinoudakis, C. (2020)
Testing And Hardening IoT Devices Against the Mirai Botnet. https://doi.org/10.1109/CyberSecurity49315.2020.9138887
A large majority of cheap Internet of Things (IoT) devices that arrive brand new, and are configured with out-of-the-box settings, are not being properly secured by the manufa...

A Distributed Trust Framework for Privacy-Preserving Machine Learning

Conference Proceeding
Abramson, W., Hall, A. J., Papadopoulos, P., Pitropakis, N., & Buchanan, W. J. (in press)
A Distributed Trust Framework for Privacy-Preserving Machine Learning. In Trust, Privacy and Security in Digital Business
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 ...

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...