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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 |
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2023 Conference Publication DOC-NAD: A hybrid deep one-class classifier for network anomaly detectionSarhan, Mohanad, Kulatilleke, Gayan, Lo, Wai Weng, Layeghy, Siamak and Portmann, Marius (2023). DOC-NAD: A hybrid deep one-class classifier for network anomaly detection. 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW), Bangalore, India, 1 - 4 May 2023. Piscataway, NJ, United States: IEEE. doi: 10.1109/ccgridw59191.2023.00016 |
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2023 Journal Article From zero-shot machine learning to zero-day attack detectionSarhan, Mohanad, Layeghy, Siamak, Gallagher, Marcus and Portmann, Marius (2023). From zero-shot machine learning to zero-day attack detection. International Journal of Information Security, 22 (4), 947-959. doi: 10.1007/s10207-023-00676-0 |
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2023 Journal Article Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoinLo, Wai Weng, Kulatilleke, Gayan K., Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin. Applied Intelligence, 53 (16), 1-12. doi: 10.1007/s10489-023-04504-9 |
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2023 Other Outputs NF-CSE-CIC-IDS2018-v2Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-CSE-CIC-IDS2018-v2. The University of Queensland. (Dataset) doi: 10.48610/e9636b7 |
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2023 Other Outputs NF-CSE-CIC-IDS2018Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-CSE-CIC-IDS2018. The University of Queensland. (Dataset) doi: 10.48610/b9ed88b |
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2022 Conference Publication Network intrusion detection system in a light bulbManocchio, Liam Daly, Layeghy, Siamak and Portmann, Marius (2022). Network intrusion detection system in a light bulb. 32nd International Telecommunication Networks and Applications Conference (ITNAC), Wellington, New Zealand, 30 November- 2 December 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/itnac55475.2022.9998371 |
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2022 Journal Article Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-Based Network Intrusion DetectionSarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2022). Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-Based Network Intrusion Detection. Big Data Research, 30 100359, 1-9. doi: 10.1016/j.bdr.2022.100359 |
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2022 Journal Article Cyber threat intelligence sharing scheme based on federated learning for network intrusion detectionSarhan, Mohanad, Layeghy, Siamak, Moustafa, Nour and Portmann, Marius (2022). Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection. Journal of Network and Systems Management, 31 (1) 3. doi: 10.1007/s10922-022-09691-3 |
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2022 Journal Article HBFL: a hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detectionSarhan, Mohanad, Lo, Wai Weng, Layeghy, Siamak and Portmann, Marius (2022). HBFL: a hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection. Computers and Electrical Engineering, 103 108379, 1-17. doi: 10.1016/j.compeleceng.2022.108379 |
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2022 Conference Publication Graph neural network-based android malware classification with jumping knowledgeLo, Wai Weng, Layeghy, Siamak, Sarhan, Mohanad, Gallagher, Marcus and Portmann, Marius (2022). Graph neural network-based android malware classification with jumping knowledge. 2022 IEEE Conference on Dependable and Secure Computing (DSC), Edinburgh, United Kingdom, 22-24 June 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/dsc54232.2022.9888878 |
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2022 Book Chapter SCOR: a constraint programming approach to software defined networkingLayeghy, Siamak and Portmann, Marius (2022). SCOR: a constraint programming approach to software defined networking. Horizons in computer science research. Volume 22. (pp. 141-191) edited by Thomas S. Clary. New York, NY United States: Nova Science Publishers. |
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2021 Journal Article Towards a standard feature set for network intrusion detection system datasetsSarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2021). Towards a standard feature set for network intrusion detection system datasets. Mobile Networks and Applications, 27 (1), 357-370. doi: 10.1007/s11036-021-01843-0 |
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2021 Conference Publication FlowGAN - Synthetic Network Flow Generation using Generative Adversarial NetworksManocchio, Liam Daly, Layeghy, Siamak and Portmann, Marius (2021). FlowGAN - Synthetic Network Flow Generation using Generative Adversarial Networks. International Conference on Computational Science and Engineering (CSE), Shenyang, China, 20-22 October 2021. Piscataway, NJ, United States: IEEE. doi: 10.1109/cse53436.2021.00033 |
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2021 Journal Article Deep learning-based cattle behaviour classification using joint time-frequency data representationHosseininoorbin, Seyedehfaezeh, Layeghy, Siamak, Kusy, Brano, Jurdak, Raja, Bishop-Hurley, Greg J., Greenwood, Paul L and Portmann, Marius (2021). Deep learning-based cattle behaviour classification using joint time-frequency data representation. Computers and Electronics in Agriculture, 187 106241, 106241. doi: 10.1016/j.compag.2021.106241 |
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2021 Journal Article Automatic fetal movement recognition from multi-channel accelerometry dataMesbah, Mostefa, Khlif, Mohamed Salah, Layeghy, Siamak, East, Christine E., Dong, Shiying, Brodtmann, Amy, Colditz, Paul B. and Boashash, Boualem (2021). Automatic fetal movement recognition from multi-channel accelerometry data. Computer Methods and Programs in Biomedicine, 210 106377, 106377. doi: 10.1016/j.cmpb.2021.106377 |
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2021 Conference Publication Scaling Spectrogram Data Representation for Deep Learning on Edge TPUHosseininoorbin, Seyedehfaezeh, Layeghy, Siamak, Kusy, Brano, Jurdak, Raja and Portmann, Marius (2021). Scaling Spectrogram Data Representation for Deep Learning on Edge TPU. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Kassel, Germany, 22-26 March 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/PerComWorkshops51409.2021.9431041 |
