Mohanad Sarhan

According to our database, Mohanad Sarhan authored at least 19 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

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

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

2023

Exploring edge TPU for network intrusion detection in IoT.

Seyedehfaezeh Hosseininoorbin, Siamak Layeghy, Mohanad Sarhan, Raja Jurdak, Marius Portmann

J. Parallel Distributed Comput., September, 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., August, 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, July, 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

From zero-shot machine learning to zero-day attack detection.

Mohanad Sarhan, Siamak Layeghy, Marcus Gallagher, Marius Portmann

Int. J. Inf. Sec., 2023

DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection.

Mohanad Sarhan, Gayan K. Kulatilleke, Wai Weng Lo, Siamak Layeghy, Marius Portmann

Proceedings of the 23rd IEEE/ACM International Symposium on Cluster, 2023

2022

Towards a Standard Feature Set for Network Intrusion Detection System Datasets.

Mohanad Sarhan, Siamak Layeghy, Marius Portmann

Mob. Networks Appl., 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

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

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

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT.

Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann

Proceedings of the 2022 IEEE/IFIP Network Operations and Management Symposium, 2022

Graph Neural Network-based Android Malware Classification with Jumping Knowledge.

Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann

Proceedings of the IEEE Conference on Dependable and Secure Computing, 2022

2021

Feature Analysis for ML-based IIoT Intrusion Detection.

Mohanad Sarhan, Siamak Layeghy, 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

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System.

Wai Weng Lo, Siamak Layeghy, Mohanad Sarhan, Marcus Gallagher, Marius Portmann

CoRR, 2021

Towards a Standard Feature Set of NIDS Datasets.

Mohanad Sarhan, Siamak Layeghy, Nour Moustafa, Marius Portmann

CoRR, 2021

2020

NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems.

Mohanad Sarhan, Siamak Layeghy, Nour Moustafa, Marius Portmann

Proceedings of the Big Data Technologies and Applications, 2020