| Traffic generation using containerization for machine learning H Clausen, R Flood, D Aspinall Proceedings of the 2019 Workshop on DYnamic and Novel Advances in Machine …, 2019 | 14 | 2019 |
| Cbam: A contextual model for network anomaly detection H Clausen, G Grov, D Aspinall Computers 10 (6), 79, 2021 | 10 | 2021 |
| Evading stepping-stone detection with enough chaff H Clausen, MS Gibson, D Aspinall Network and System Security: 14th International Conference, NSS 2020 …, 2020 | 8 | 2020 |
| Better anomaly detection for access attacks using deep bidirectional LSTMs H Clausen, G Grov, M Sabate, D Aspinall International Conference on Machine Learning for Networking, 1-18, 2020 | 7 | 2020 |
| Controlling network traffic microstructures for machine-learning model probing H Clausen, R Flood, D Aspinall International Conference on Security and Privacy in Communication Systems …, 2021 | 4 | 2021 |
| Examining traffic microstructures to improve model development H Clausen, D Aspinall 2021 IEEE Security and Privacy Workshops (SPW), 19-24, 2021 | 4 | 2021 |
| Bayesian activity modelling for network flow data H Clausen, M Briers, NM Adams Data Science for Cyber-Security, 55-76, 2019 | 3 | 2019 |
| A containerised approach to labelled C&C traffic ML Asprusten, JL Gjerstad, G Grov, EH Kjellstadli, R Flood, H Clausen, ... | 2 | 2022 |
| Detecting proxies relaying streaming internet traffic H Clausen, A Manocha, M Gibson IEEE INFOCOM 2022-IEEE Conference on Computer Communications Workshops …, 2022 | 1 | 2022 |
| Traffic microstructures and network anomaly detection H Clausen The University of Edinburgh, 2022 | | 2022 |
