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2025 Other Outputs NF-UNSW-NB15-v3Luay, Majed, Layeghy, Siamak, Mohanad, Sarhan, Sayedehfaezeh, Hoseininoorbin, Moustafa, Nour and Portmann, Marius (2025). NF-UNSW-NB15-v3. The University of Queensland. (Dataset) doi: 10.48610/6e0eda1 |
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2024 Conference Publication Towards Explainable Network Intrusion Detection using Large Language ModelsHoussel, Paul R. B., Singh, Priyanka, Layeghy, Siamak and Portmann, Marius (2024). Towards Explainable Network Intrusion Detection using Large Language Models. 2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), Sharjah, United Arab Emirates, 16-19 December 2024. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/bdcat63179.2024.00021 |
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2024 Journal Article FlowTransformer: A flexible python framework for flow-based network data analysisManocchio, Liam Daly, Layeghy, Siamak and Portmann, Marius (2024). FlowTransformer: A flexible python framework for flow-based network data analysis. Software Impacts, 22 100702, 100702. doi: 10.1016/j.simpa.2024.100702 |
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2024 Journal Article Does every hotel room need a minifridge? Empirical evidence from consumer self-reports and an automatic sensor-based system measuring electricity consumption and guest useDolnicar, Sara, Greene, Danyelle, Layeghy, Siamak and Portmann, Marius (2024). Does every hotel room need a minifridge? Empirical evidence from consumer self-reports and an automatic sensor-based system measuring electricity consumption and guest use. Annals of Tourism Research Empirical Insights, 5 (2) 100134. doi: 10.1016/j.annale.2024.100134 |
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2024 Journal Article A configurable anonymisation approach for network flow data: Balancing utility and privacyManocchio, Liam Daly, Layeghy, Siamak, Gwynne, David and Portmann, Marius (2024). A configurable anonymisation approach for network flow data: Balancing utility and privacy. Computers and Electrical Engineering, 118 109465, 1-16. doi: 10.1016/j.compeleceng.2024.109465 |
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2024 Journal Article Feature extraction for machine learning-based intrusion detection in IoT networksSarhan, Mohanad, Layeghy, Siamak, Moustafa, Nour, Gallagher, Marcus and Portmann, Marius (2024). Feature extraction for machine learning-based intrusion detection in IoT networks. Digital Communications and Networks, 10 (1), 205-216. doi: 10.1016/j.dcan.2022.08.012 |
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2023 Journal Article Exploring Edge TPU for Network Intrusion Detection in IoTHosseininoorbin, Seyedehfaezeh, Layeghy, Siamak, Sarhan, Mohanad, Jurdak, Raja and Portmann, Marius (2023). Exploring Edge TPU for Network Intrusion Detection in IoT. Journal of Parallel and Distributed Computing, 179 104712, 1-11. doi: 10.1016/j.jpdc.2023.05.001 |
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2023 Journal Article HARBIC: Human activity recognition using bi-stream convolutional neural network with dual joint time-frequency representationHosseininoorbin, Seyedehfaezeh, Layeghy, Siamak, Kusy, Brano, Jurdak, Raja and Portmann, Marius (2023). HARBIC: Human activity recognition using bi-stream convolutional neural network with dual joint time-frequency representation. Internet of Things, 22 100816, 1-17. doi: 10.1016/j.iot.2023.100816 |
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2023 Journal Article Exploring Edge TPU for deep feed-forward neural networksHosseininoorbin, Seyedehfaezeh, Layeghy, Siamak, Kusy, Brano, Jurdak, Raja and Portmann, Marius (2023). Exploring Edge TPU for deep feed-forward neural networks. Internet of Things, 22 100749, 1-16. doi: 10.1016/j.iot.2023.100749 |
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2023 Journal Article XG-BoT: an explainable deep graph neural network for botnet detection and forensicsLo, Wai Weng, Kulatilleke, Gayan, Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). XG-BoT: an explainable deep graph neural network for botnet detection and forensics. Internet of Things, 22 100747, 100747. doi: 10.1016/j.iot.2023.100747 |
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2023 Other Outputs NF-BoT-IoTSarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-BoT-IoT. The University of Queensland. (Dataset) doi: 10.48610/62e6d80 |
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2023 Other Outputs CIC-BoT-IoTSarhan, Mohanad, Layeghy, Siamak and Portmann, Marus (2023). CIC-BoT-IoT. The University of Queensland. (Dataset) doi: 10.48610/c80fccd |
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2023 Other Outputs NF-UNSW-NB15-v2Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-UNSW-NB15-v2. The University of Queensland. (Dataset) doi: 10.48610/ffbb0c1 |
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2023 Other Outputs NF-ToN-IoTSarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-ToN-IoT. The University of Queensland. (Dataset) doi: 10.48610/2fa2ed6 |
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2023 Other Outputs NF-UQ-NIDS-v2Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-UQ-NIDS-v2. The University of Queensland. (Dataset) doi: 10.48610/631a24a |
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2023 Other Outputs NF-UNSW-NB15Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-UNSW-NB15. The University of Queensland. (Dataset) doi: 10.48610/5d0832d |
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2023 Other Outputs NF-ToN-IoT-v2Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-ToN-IoT-v2. The University of Queensland. (Dataset) doi: 10.48610/38a2d07 |
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2023 Other Outputs NF-UQ-NIDSSarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-UQ-NIDS. The University of Queensland. (Dataset) doi: 10.48610/69b5a53 |
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2023 Other Outputs CIC-ToN-IoTSarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). CIC-ToN-IoT. The University of Queensland. (Dataset) doi: 10.48610/f6884ce |
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2023 Other Outputs NF-BoT-IoT-v2Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-BoT-IoT-v2. The University of Queensland. (Dataset) doi: 10.48610/ec73920 |
