| NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems M Sarhan, S Layeghy, N Moustafa, M Portmann Big Data Technologies and Applications: 10th EAI International Conference …, 2021 | 371 | 2021 |
| E-graphsage: A graph neural network based intrusion detection system for iot WW Lo, S Layeghy, M Sarhan, M Gallagher, M Portmann NOMS 2022-2022 IEEE/IFIP Network Operations and Management Symposium, 1-9, 2022 | 292 | 2022 |
| Towards a standard feature set for network intrusion detection system datasets M Sarhan, S Layeghy, M Portmann Mobile Networks and Applications 27 (1), 357-370, 2022 | 292 | 2022 |
| Feature extraction for machine learning-based intrusion detection in IoT networks M Sarhan, S Layeghy, N Moustafa, M Gallagher, M Portmann Digital Communications and Networks 10 (1), 205-216, 2024 | 128 | 2024 |
| Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection M Sarhan, S Layeghy, N Moustafa, M Portmann Journal of Network and Systems Management 31 (1), 3, 2023 | 95 | 2023 |
| Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection M Sarhan, S Layeghy, M Portmann arXiv preprint arXiv:2104.07183, 2021 | 88 | 2021 |
| HBFL: A hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection M Sarhan, WW Lo, S Layeghy, M Portmann Computers and Electrical Engineering 103, 108379, 2022 | 68 | 2022 |
| Feature Analysis for Machine Learning-based IoT Intrusion Detection M Sarhan, S Layeghy, M Portmann arXiv preprint arXiv:2108.12732, 2021 | 64 | 2021 |
| From zero-shot machine learning to zero-day attack detection M Sarhan, S Layeghy, M Gallagher, M Portmann International Journal of Information Security 22 (4), 947-959, 2023 | 58 | 2023 |
| Flowtransformer: A transformer framework for flow-based network intrusion detection systems LD Manocchio, S Layeghy, WW Lo, GK Kulatilleke, M Sarhan, ... Expert Systems with Applications 241, 122564, 2024 | 47 | 2024 |
| XG-BoT: An explainable deep graph neural network for botnet detection and forensics WW Lo, G Kulatilleke, M Sarhan, S Layeghy, M Portmann Internet of Things 22, 100747, 2023 | 46 | 2023 |
| Inspection-L: A Self-Supervised GNN-Based Money Laundering Detection System for Bitcoin WW Lo, M Sarhan, S Layeghy, M Portmann | 44* | |
| Exploring edge TPU for network intrusion detection in IoT S Hosseininoorbin, S Layeghy, M Sarhan, R Jurdak, M Portmann Journal of Parallel and Distributed Computing 179, 104712, 2023 | 31 | 2023 |
| Graph neural network-based android malware classification with jumping knowledge WW Lo, S Layeghy, M Sarhan, M Gallagher, M Portmann 2022 IEEE Conference on Dependable and Secure Computing (DSC), 1-9, 2022 | 29 | 2022 |
| Feature analysis for ML-based IIoT intrusion detection M Sarhan, S Layeghy, M Portmann arXiv e-prints, arXiv: 2108.12732, 2021 | 17 | 2021 |
| Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection. arXiv 2021 M Sarhan, S Layeghy, M Portmann arXiv preprint arXiv:2104.07183, 2022 | 9 | 2022 |
| DOC-NAD: A hybrid deep one-class classifier for network anomaly detection M Sarhan, G Kulatilleke, WW Lo, S Layeghy, M Portmann 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet …, 2023 | 5 | 2023 |
| Towards a standard feature set of NIDS datasets. CoRR, abs/2101.11315 M Sarhan, S Layeghy, N Moustafa, M Portmann arXiv preprint arXiv:2101.11315, 2021 | 3 | 2021 |
| Nf-uq-nids-v2 M Sarhan, S Layeghy, M Portmann The University of Queensland, 2023 | 2 | 2023 |
| The detection of network cyber attacks using machine learning M Sarhan | 1 | 2023 |
Marius PortmannThe University of QueenslandPatvirtintas el. paštas eecs.uq.edu.au