Naghmeh Moradpoor

Hamish Alsop, Leandros Maglaras, Naghmeh Moradpoor

This paper presents Ember, a serverless peer-to-peer messaging system providing end-to-end encrypted communication over a decentralised IPv6 mesh network. Ember operates without central servers, enforces data minimisation through ciphertext-only local storage and time-based message expiration, and prioritises architectural clarity, explicit trust boundaries, and practical deployability on Android. The paper describes the system architecture, cryptographic design, network model, and security properties -- including dynamic testing results demonstrating that no plaintext is recoverable from captured network traffic -- and discusses limitations and future work

Adam James Hall, Nikolaos Pitropakis, William J Buchanan et al.

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 exists between the requirements of this community and the solutions that are currently available. This paper uses the CERT dataset r4.2 along with a series of machine learning classifiers to predict the occurrence of a particular malicious insider threat scenario - the uploading sensitive information to wiki leaks before leaving the organization. These algorithms are aggregated into a meta-classifier which has a stronger predictive performance than its constituent models. It also defines a methodology for performing pre-processing on organizational log data into daily user summaries for classification, and is used to train multiple classifiers. Boosting is also applied to optimise classifier accuracy. Overall the models are evaluated through analysis of their associated confusion matrix and Receiver Operating Characteristic (ROC) curve, and the best performing classifiers are aggregated into an ensemble classifier. This meta-classifier has an accuracy of \textbf{96.2\%} with an area under the ROC curve of \textbf{0.988}.