All Outputs (2)

A Taxonomy and Survey of Attacks Against Machine Learning (2019)
Journal Article
Pitropakis, N., Panaousis, E., Giannetsos, T., Anastasiadis, E., & Loukas, G. (2019). A Taxonomy and Survey of Attacks Against Machine Learning. Computer Science Review, 34, https://doi.org/10.1016/j.cosrev.2019.100199

The majority of machine learning methodologies operate with the assumption that their environment is benign. However, this assumption does not always hold, as it is often advantageous to adversaries to maliciously modify the training (poisoning attac... Read More about A Taxonomy and Survey of Attacks Against Machine Learning.

Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier (2019)
Presentation / Conference Contribution
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.

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 cu... Read More about Predicting Malicious Insider Threat Scenarios Using Organizational Data and a Heterogeneous Stack-Classifier.