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
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
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
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
2023
Other Outputs
CIC-BoT-IoT
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marus (2023). CIC-BoT-IoT. The University of Queensland. (Dataset) doi: 10.48610/c80fccd
2023
Other Outputs
NF-UNSW-NB15-v2
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-UNSW-NB15-v2. The University of Queensland. (Dataset) doi: 10.48610/ffbb0c1
2023
Other Outputs
NF-ToN-IoT
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-ToN-IoT. The University of Queensland. (Dataset) doi: 10.48610/2fa2ed6
2023
Other Outputs
NF-UQ-NIDS-v2
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-UQ-NIDS-v2. The University of Queensland. (Dataset) doi: 10.48610/631a24a
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
Conference Publication
DOC-NAD: A hybrid deep one-class classifier for network anomaly detection
Sarhan, 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
2023
Journal Article
From zero-shot machine learning to zero-day attack detection
Sarhan, 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
2023
Journal Article
Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin
Lo, 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
2023
Other Outputs
NF-CSE-CIC-IDS2018-v2
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-CSE-CIC-IDS2018-v2. The University of Queensland. (Dataset) doi: 10.48610/e9636b7
2023
Other Outputs
NF-CSE-CIC-IDS2018
Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-CSE-CIC-IDS2018. The University of Queensland. (Dataset) doi: 10.48610/b9ed88b
2022
Conference Publication
Network intrusion detection system in a light bulb
Manocchio, 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
2022
Journal Article
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-Based Network Intrusion Detection
Sarhan, 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
2022
Journal Article
Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection
Sarhan, 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
Funding
Current funding
Past funding
Supervision
Availability
- Dr Siamak Layeghy is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
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: Professor Marius Portmann
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Doctor Philosophy
Machine Learning for Improving Services and Management of Software Defined Networks
Principal Advisor
Other advisors: 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, Professor Marius Portmann
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Doctor Philosophy
Towards Autonomous Network Security
Associate Advisor
Other advisors: Associate Professor Marcus Gallagher, 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, Professor Marius Portmann
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Doctor Philosophy
Towards Autonomous Network Security
Associate Advisor
Other advisors: Associate Professor Marcus Gallagher, Professor Marius Portmann
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Doctor Philosophy
Towards Practical Machine Learning Based Network Intrusion Detection
Associate Advisor
Other advisors: Associate Professor Marcus Gallagher, 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: 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, 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, Professor Marius Portmann
Media
Enquiries
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