Diana Spears

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A novel approach is presented that bridges the gap between anomaly and misuse detection for identifying cyber attacks. The approach consists of an ensemble of classifiers that, together, produce a more informative output regarding the class of attack than any of the classifiers alone. Each classifier classifies based on a limited subset of possible features(More)
We examine the plausibility of using an Artificial Neural Network (ANN) and an Importance-Aided Neural Network (IANN) for the refinement of the structural model used to create full-wave tomography images. Specifically, we apply the machine learning techniques to classifying segments of observed data wave seismograms and synthetic data wave seismograms as(More)
Preface As intelligent autonomous agents and multi-agent systems applications become more pervasive, it becomes increasingly important to understand the risks associated with using these systems. Incorrect or inappropriate agent behaviour can have harmful effects, including financial cost, loss of data, and injury to humans or systems. Thus, security and(More)
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