NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems. - Mohanad Sarhan; Siamak Layeghy; Nour Moustafa; Marius Portmann (BDTA/WiCON, 2020)

Towards a Standard Feature Set of NIDS Datasets. - Mohanad Sarhan; Siamak Layeghy; Nour Moustafa; Marius Portmann (CoRR, 2021)

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System. - Wai Weng Lo; Siamak Layeghy; Mohanad Sarhan; Marcus Gallagher; Marius Portmann (CoRR, 2021)

An Explainable Machine Learning-based Network Intrusion Detection System for Enabling Generalisability in Securing IoT Networks. - Mohanad Sarhan; Siamak Layeghy; Marius Portmann (CoRR, 2021)

Feature Analysis for ML-based IIoT Intrusion Detection. - Mohanad Sarhan; Siamak Layeghy; Marius Portmann (CoRR, 2021)

Graph Neural Network-based Android Malware Classification with Jumping Knowledge. - Wai Weng Lo; Siamak Layeghy; Mohanad Sarhan; Marcus Gallagher; Marius Portmann (DSC, 2022)

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT. - Wai Weng Lo; Siamak Layeghy; Mohanad Sarhan; Marcus Gallagher; Marius Portmann (NOMS, 2022)

Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-Based Network Intrusion Detection. - Mohanad Sarhan; Siamak Layeghy; Marius Portmann (Big Data Res., 2022)

HBFL: A hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection. - Mohanad Sarhan; Wai Weng Lo; Siamak Layeghy; Marius Portmann (Comput. Electr. Eng., 2022)

XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics. - Wai Weng Lo; Siamak Layeghy; Mohanad Sarhan; Marius Portmann (CoRR, 2022)

Towards a Standard Feature Set for Network Intrusion Detection System Datasets. - Mohanad Sarhan; Siamak Layeghy; Marius Portmann (Mob. Networks Appl., 2022)

DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection. - Mohanad Sarhan; Gayan K. Kulatilleke; Wai Weng Lo; Siamak Layeghy; Marius Portmann (CCGridW, 2023)

From zero-shot machine learning to zero-day attack detection. - Mohanad Sarhan; Siamak Layeghy; Marcus Gallagher; Marius Portmann (Int. J. Inf. Sec., 2023)

Cyber Threat Intelligence Sharing Scheme Based on Federated Learning for Network Intrusion Detection. - Mohanad Sarhan; Siamak Layeghy; Nour Moustafa; Marius Portmann (J. Netw. Syst. Manag., 2023)

XG-BoT: An explainable deep graph neural network for botnet detection and forensics. - Wai Weng Lo; Gayan K. Kulatilleke; Mohanad Sarhan; Siamak Layeghy; Marius Portmann (Internet Things, 2023)

Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin. - Wai Weng Lo; Gayan K. Kulatilleke; Mohanad Sarhan; Siamak Layeghy; Marius Portmann (Appl. Intell., 2023)

Exploring edge TPU for network intrusion detection in IoT. - Seyedehfaezeh Hosseininoorbin; Siamak Layeghy; Mohanad Sarhan; Raja Jurdak; Marius Portmann (J. Parallel Distributed Comput., 2023)

Feature extraction for machine learning-based intrusion detection in IoT networks. - Mohanad Sarhan; Siamak Layeghy; Nour Moustafa; Marcus Gallagher; Marius Portmann (Digit. Commun. Networks, 2024)

FlowTransformer: A transformer framework for flow-based network intrusion detection systems. - Liam Daly Manocchio; Siamak Layeghy; Wai Weng Lo; Gayan K. Kulatilleke; Mohanad Sarhan; Marius Portmann (Expert Syst. Appl., 2024)

Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems. - Majed Luay; Siamak Layeghy; Seyedehfaezeh Hosseininoorbin; Mohanad Sarhan; Nour Moustafa; Marius Portmann (CoRR, 2025)