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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-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-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 Journal Article Explainable cross-domain evaluation of ML-based network intrusion detection systemsLayeghy, Siamak and Portmann, Marius (2023). Explainable cross-domain evaluation of ML-based network intrusion detection systems. Computers and Electrical Engineering, 108 108692, 1-15. doi: 10.1016/j.compeleceng.2023.108692 |
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2023 Journal Article DI-NIDS: domain invariant network intrusion detection systemLayeghy, Siamak, Baktashmotlagh, Mahsa and Portmann, Marius (2023). DI-NIDS: domain invariant network intrusion detection system. Knowledge-Based Systems, 273 110626, 110626. doi: 10.1016/j.knosys.2023.110626 |
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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 FusedAR: energy-positive human activity recognition using kinetic and solar signal fusionSandhu, Muhammad Moid, Khalifa, Sara, Geissdoerfer, Kai, Jurdak, Raja, Portmann, Marius and Kusy, Brano (2023). FusedAR: energy-positive human activity recognition using kinetic and solar signal fusion. IEEE Sensors Journal, 23 (11), 12411-12426. doi: 10.1109/jsen.2023.3268687 |
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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 Book Chapter IntroductionSandhu, Muhammad Moid, Khalifa, Sara, Portmann, Marius and Jurdak, Raja (2023). Introduction. Self-Powered Internet of Things. (pp. 3-12) Cham: Springer International Publishing. doi: 10.1007/978-3-031-27685-9_1 |
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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 Journal Article Anomal-E: A self-supervised network intrusion detection system based on graph neural networksCaville, Evan, Lo, Wai Weng, Layeghy, Siamak and Portmann, Marius (2022). Anomal-E: A self-supervised network intrusion detection system based on graph neural networks. Knowledge-Based Systems, 258 110030, 1-11. doi: 10.1016/j.knosys.2022.110030 |
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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 Journal Article Local reference free in-field calibration of low cost air pollution monitoring sensorsGhosh, Sushmita, Das, Payali, De, Swades, Chatterjee, Shouri and Portmann, Marius (2022). Local reference free in-field calibration of low cost air pollution monitoring sensors. IEEE Transactions on Instrumentation and Measurement, 71 2517613, 1-13. doi: 10.1109/tim.2022.3203446 |
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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 Conference Publication E-GraphSAGE: a graph neural network based intrusion detection system for IoTLo, Wai Weng, Layeghy, Siamak, Sarhan, Mohanad, Gallagher, Marcus and Portmann, Marius (2022). E-GraphSAGE: a graph neural network based intrusion detection system for IoT. NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 25-29 April 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/noms54207.2022.9789878 |
