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
2014
Conference Publication
Classification of fetal movement accelerometry through time-frequency features
Layeghy, Siamak, Azemi, Ghasem, Colditz, Paul and Boashash, Boualem (2014). Classification of fetal movement accelerometry through time-frequency features. International Conference on Signal Processing and Communication Systems (ICSPCS), Gold Coast, QLD, Australia, 15-17 December 2014. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICSPCS.2014.7021055
2012
Conference Publication
A passive DSP approach to fetal movement detection for monitoring fetal health
Khlif, Mohamed Salah H., Boashash, Boualem, Layeghy, Siamak, Ben-Jabeur, Taoufik, Colditz, Paul B. and East, Christine (2012). A passive DSP approach to fetal movement detection for monitoring fetal health. 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), Montreal, Canada, 2-5 July 2012. Piscataway, NJ, Australia: IEEE. doi: 10.1109/ISSPA.2012.6310647
2012
Conference Publication
EEG amplitude and correlation spatial decay analysis for neonatal head modelling
Odabaee, Maryam, Layeghy, Siamak, Mesbah, Mostefa, Azemi, Ghasem, Boashash, Boualem, Colditz, Paul and Vanhatalo, Sampsa (2012). EEG amplitude and correlation spatial decay analysis for neonatal head modelling. 11th International Conference on Information Science, Signal Processing and their Applications, ISSPA 2012, Montreal, QC Canada, 2 - 5 July 2012. Piscataway, NJ United States: I E E E. doi: 10.1109/ISSPA.2012.6310679
2011
Conference Publication
Time-Frequency Characterization of Tri-Axial Accelerometer Data for Fetal Movement Detection
Khlif, M.S., Boashash, B., Layeghy, S., Ben-Jabeur, T., Mesbah, M., East, C. and Colditz, P. (2011). Time-Frequency Characterization of Tri-Axial Accelerometer Data for Fetal Movement Detection. IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Bilbao, Spain, 14-17 December 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/ISSPIT.2011.6151607
2011
Conference Publication
A time frequency approach to CFAR detection
Layeghy, S., Odabaee, M., Khlif, M.S. and Amindavar, H.R. (2011). A time frequency approach to CFAR detection. 11th IEEE International Symposium on Signal Processing and Information Technology (ISSPIT 2011), Bilbao, Spain, 14-17 December 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/ISSPIT.2011.6151565
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
Machine Learning for Improving Services and Management of Software Defined Networks
Principal Advisor
Other advisors: Professor Marius Portmann
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Doctor Philosophy
Enhancing the Privacy-Preserving ML techniques with Functional Encryption approach
Principal Advisor
Other advisors: Professor Marius Portmann
-
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
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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
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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