Overview
Availability
- Dr Siamak Layeghy is:
- Available for supervision
Qualifications
- Doctor of Philosophy, The University of Queensland
Research interests
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AI and Machine Learning (AI/ML) for CyberSecurity
Application of various ML techniques such as Large Language models (LLMs) and Transformers, Graph Neural Networks (GNNs), Generative Adversarial Networks(GANs), Domain Adaptation (DA), Transfer Learning, and Distributed and Federated Learning for network and host security.
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Intrusion Detection Systems (NIDS and HIDS)
Network and Host Security, in particular intrusion detection systems, based on machine learning (ML) and Artificial Intelligence (AI)
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Edge Learning and Internet of Things (IoT)
AI/ML at the edge of IoT including inference and learning, and IoT security
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Software Defined Networking (SDN)
Network optimisation, QoS Routing, Constrained Routing, P4 and Programmable Data Planes
Works
Search Professor Siamak Layeghy’s works on UQ eSpace
Featured
2024
Journal Article
FlowTransformer: A transformer framework for flow-based network intrusion detection systems
Manocchio, Liam Daly, Layeghy, Siamak, Lo, Wai Weng, Kulatilleke, Gayan K., Sarhan, Mohanad and Portmann, Marius (2024). FlowTransformer: A transformer framework for flow-based network intrusion detection systems. Expert Systems with Applications, 241 122564, 122564. doi: 10.1016/j.eswa.2023.122564
2024
Journal Article
Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasets
Layeghy, Siamak, Gallagher, Marcus and Portmann, Marius (2024). Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasets. Journal of Information Security and Applications, 80 103689, 1-18. doi: 10.1016/j.jisa.2023.103689
2023
Journal Article
Exploring Edge TPU for Network Intrusion Detection in IoT
Hosseininoorbin, Seyedehfaezeh, Layeghy, Siamak, Sarhan, Mohanad, Jurdak, Raja and Portmann, Marius (2023). Exploring Edge TPU for Network Intrusion Detection in IoT. Journal of Parallel and Distributed Computing, 179 104712, 1-11. doi: 10.1016/j.jpdc.2023.05.001
Featured
2023
Journal Article
Explainable cross-domain evaluation of ML-based network intrusion detection systems
Layeghy, 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
Featured
2023
Journal Article
DI-NIDS: domain invariant network intrusion detection system
Layeghy, 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
Featured
2022
Journal Article
Anomal-E: A self-supervised network intrusion detection system based on graph neural networks
Caville, 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
2022
Conference Publication
E-GraphSAGE: a graph neural network based intrusion detection system for IoT
Lo, 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
2021
Conference Publication
NetFlow datasets for machine learning-based network intrusion detection systems
Sarhan, Mohanad, Layeghy, Siamak, Moustafa, Nour and Portmann, Marius (2021). NetFlow datasets for machine learning-based network intrusion detection systems. 10th EAI International Conference, BDTA 2020 and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, 11 December 2020. Cham, Switzerland: Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-3-030-72802-1_9
2024
Journal Article
Does every hotel room need a minifridge? Empirical evidence from consumer self-reports and an automatic sensor-based system measuring electricity consumption and guest use
Dolnicar, Sara, Greene, Danyelle, Layeghy, Siamak and Portmann, Marius (2024). Does every hotel room need a minifridge? Empirical evidence from consumer self-reports and an automatic sensor-based system measuring electricity consumption and guest use. Annals of Tourism Research Empirical Insights, 5 (2) 100134, 100134. doi: 10.1016/j.annale.2024.100134
2024
Journal Article
A configurable anonymisation approach for network flow data: Balancing utility and privacy
Manocchio, Liam Daly, Layeghy, Siamak, Gwynne, David and Portmann, Marius (2024). A configurable anonymisation approach for network flow data: Balancing utility and privacy. Computers and Electrical Engineering, 118 109465, 1-16. doi: 10.1016/j.compeleceng.2024.109465
2024
Journal Article
Feature extraction for machine learning-based intrusion detection in IoT networks
Sarhan, Mohanad, Layeghy, Siamak, Moustafa, Nour, Gallagher, Marcus and Portmann, Marius (2024). Feature extraction for machine learning-based intrusion detection in IoT networks. Digital Communications and Networks, 10 (1), 205-216. doi: 10.1016/j.dcan.2022.08.012
2023
Journal Article
XG-BoT: an explainable deep graph neural network for botnet detection and forensics
Lo, Wai Weng, Kulatilleke, Gayan, Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). XG-BoT: an explainable deep graph neural network for botnet detection and forensics. Internet of Things, 22 100747, 100747. doi: 10.1016/j.iot.2023.100747
2023
Journal Article
HARBIC: Human activity recognition using bi-stream convolutional neural network with dual joint time-frequency representation
Hosseininoorbin, Seyedehfaezeh, Layeghy, Siamak, Kusy, Brano, Jurdak, Raja and Portmann, Marius (2023). HARBIC: Human activity recognition using bi-stream convolutional neural network with dual joint time-frequency representation. Internet of Things, 22 100816, 1-17. doi: 10.1016/j.iot.2023.100816
2023
Journal Article
Exploring Edge TPU for deep feed-forward neural networks
Hosseininoorbin, Seyedehfaezeh, Layeghy, Siamak, Kusy, Brano, Jurdak, Raja and Portmann, Marius (2023). Exploring Edge TPU for deep feed-forward neural networks. Internet of Things, 22 100749, 1-16. doi: 10.1016/j.iot.2023.100749
2023
Other Outputs
NF-UNSW-NB15
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-UNSW-NB15. The University of Queensland. (Dataset) doi: 10.48610/5d0832d
2023
Other Outputs
NF-ToN-IoT-v2
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-ToN-IoT-v2. The University of Queensland. (Dataset) doi: 10.48610/38a2d07
2023
Other Outputs
NF-UQ-NIDS
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-UQ-NIDS. The University of Queensland. (Dataset) doi: 10.48610/69b5a53
2023
Other Outputs
CIC-ToN-IoT
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). CIC-ToN-IoT. The University of Queensland. (Dataset) doi: 10.48610/f6884ce
2023
Other Outputs
NF-BoT-IoT-v2
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-BoT-IoT-v2. The University of Queensland. (Dataset) doi: 10.48610/ec73920
2023
Other Outputs
NF-BoT-IoT
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-BoT-IoT. The University of Queensland. (Dataset) doi: 10.48610/62e6d80
Supervision
Availability
- Dr Siamak Layeghy is:
- Available for supervision
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Available projects
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Machine Learning for Improving Services and Management of Software Defined Networks
By leveraging the power of P4 programming, this project aims to harness advanced machine learning techniques and large language models to enhance the services and management of software-defined networks in the real-world applications.
Supervision history
Current supervision
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Doctor Philosophy
Enhancing the Privacy-Preserving ML techniques with Functional Encryption approach
Principal Advisor
Other advisors: Associate Professor Marius Portmann
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Doctor Philosophy
Towards Autonomous Network Security
Associate Advisor
Other advisors: Associate Professor Marcus Gallagher, Associate Professor Marius Portmann
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Doctor Philosophy
Exploring the Capabilities of LoRaWAN IoT Technology for Multisensor Data Collection and Analysis
Associate Advisor
Other advisors: Professor Sara Dolnicar, Associate Professor Marius Portmann
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Doctor Philosophy
Low-energy LoRaWAN-based automatic and continuous measurement of organisational environmental performance.
Associate Advisor
Other advisors: Professor Sara Dolnicar, Associate Professor Marius Portmann
Completed supervision
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2023
Doctor Philosophy
Deep Learning at the Edge: Exploring in-situ Classification in IoT
Associate Advisor
Other advisors: Associate Professor Marius Portmann
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2023
Doctor Philosophy
The Detection of Network Cyber Attacks Using Machine Learning
Associate Advisor
Other advisors: Associate Professor Marcus Gallagher, Associate Professor Marius Portmann
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2023
Master Philosophy
Graph Representation Learning for Cyberattack Detection and Forensics
Associate Advisor
Other advisors: Associate Professor Marcus Gallagher, Associate Professor Marius Portmann
Media
Enquiries
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