Professor Marius Portmann
Professor

Marius Portmann

Email: 
Phone: 
+61 7 336 51636

Background

Prof Portmann is the UQ-Cisco Chair of Network Security at the School of Electrical Engineering and Computer Science (EECS) at The University of Queensland (UQ).

He received his PhD in Electrical Engineering from the Swiss Federal Institute of Technology (ETH) in Zürich in 2003. His research interests include Computer Networks, Cybersecurity, IoT (Internet of Things) and applied AI.

Availability

Professor Marius Portmann is:
Available for supervision

Fields of research

Communications engineering Distributed computing and systems software Engineering

Qualifications

  • Masters (Coursework) of Science, Swiss Federal Institute of Technology ETH Zürich
  • Doctor of Philosophy, Swiss Federal Institute of Technology ETH Zürich

Research interests

  • Cyber Security

    Machine Learning based Intrusion Detection

  • Computer Networks

    IoT Networks, Software Defined Networking (SDN)

  • Blockchain Technology

Search Professor Marius Portmann’s works on UQ eSpace

190 works between 2002 and 2025
All (190) Book (1) Journal Article (62) Other Outputs (17) Conference Publication (103) Book Chapter (7)

41 - 60 of 190 works

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

NF-BoT-IoT

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

CIC-ToN-IoT

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

Explainable cross-domain evaluation of ML-based network intrusion detection systems

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

DI-NIDS: domain invariant network intrusion detection system

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

DOC-NAD: A hybrid deep one-class classifier for network anomaly detection

2023

Journal Article

FusedAR: energy-positive human activity recognition using kinetic and solar signal fusion

Sandhu, Muhammad Moid, Khalifa, Sara, Geissdoerfer, Kai, Jurdak, Raja, Portmann, Marius and Kusy, Brano (2023). FusedAR: energy-positive human activity recognition using kinetic and solar signal fusion. IEEE Sensors Journal, 23 (11), 12411-12426. doi: 10.1109/jsen.2023.3268687

FusedAR: energy-positive human activity recognition using kinetic and solar signal fusion

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

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

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

Inspection-L: self-supervised GNN node embeddings for money laundering detection in bitcoin

2023

Book Chapter

Introduction

Sandhu, Muhammad Moid, Khalifa, Sara, Portmann, Marius and Jurdak, Raja (2023). Introduction. Self-Powered Internet of Things. (pp. 3-12) Cham: Springer International Publishing. doi: 10.1007/978-3-031-27685-9_1

Introduction

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

NF-CSE-CIC-IDS2018-v2

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

NF-CSE-CIC-IDS2018

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

Anomal-E: A self-supervised network intrusion detection system based on graph neural networks

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

Network intrusion detection system in a light bulb

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

Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-Based Network Intrusion Detection

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

Cyber threat intelligence sharing scheme based on federated learning for network intrusion detection

2022

Journal Article

HBFL: a hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection

Sarhan, Mohanad, Lo, Wai Weng, Layeghy, Siamak and Portmann, Marius (2022). HBFL: a hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection. Computers and Electrical Engineering, 103 108379, 1-17. doi: 10.1016/j.compeleceng.2022.108379

HBFL: a hierarchical blockchain-based federated learning framework for collaborative IoT intrusion detection

2022

Journal Article

Local reference free in-field calibration of low cost air pollution monitoring sensors

Ghosh, Sushmita, Das, Payali, De, Swades, Chatterjee, Shouri and Portmann, Marius (2022). Local reference free in-field calibration of low cost air pollution monitoring sensors. IEEE Transactions on Instrumentation and Measurement, 71 2517613, 1-13. doi: 10.1109/tim.2022.3203446

Local reference free in-field calibration of low cost air pollution monitoring sensors

2022

Conference Publication

Graph neural network-based android malware classification with jumping knowledge

Lo, Wai Weng, Layeghy, Siamak, Sarhan, Mohanad, Gallagher, Marcus and Portmann, Marius (2022). Graph neural network-based android malware classification with jumping knowledge. 2022 IEEE Conference on Dependable and Secure Computing (DSC), Edinburgh, United Kingdom, 22-24 June 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers . doi: 10.1109/dsc54232.2022.9888878

Graph neural network-based android malware classification with jumping knowledge

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

E-GraphSAGE: a graph neural network based intrusion detection system for IoT

2022

Conference Publication

FDGATII: Fast Dynamic Graph Attention with Initial Residual and Identity

Kulatilleke, Gayan K., Portmann, Marius, Ko, Ryan and Chandra, Shekhar S. (2022). FDGATII: Fast Dynamic Graph Attention with Initial Residual and Identity. 35th Australasian Joint Conference on Artificial Intelligence: AI 2022, Perth, WA Australia, 5–8 December 2022. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-22695-3_6

FDGATII: Fast Dynamic Graph Attention with Initial Residual and Identity

Current funding

  • 2025 - 2028
    Mechanisms of Behaviour Change Theory
    ARC Discovery Projects
    Open grant
  • 2024 - 2025
    Customer electricity usage segmentation based on smart meter data
    Energy Queensland Limited
    Open grant
  • 2021 - 2025
    Reducing plate waste in hotels - which interventions are most effective?
    ARC Linkage Projects
    Open grant

Past funding

  • 2022 - 2023
    Blockchain-based Event Ticketing System
    Innovation Connections
    Open grant
  • 2019
    Machine Learning for Automated Network Anomaly Detection, Cyber Security and Analysis - Phase II
    Innovation Connections
    Open grant
  • 2018 - 2019
    Machine Learning for Automated Network Anomaly detection and Analysis
    Innovation Connections
    Open grant
  • 2018
    Smart Lending
    Commonwealth Bank of Australia
    Open grant
  • 2017 - 2020
    Software Defined Networking for the Internet of Things
    Data 61 - University Collaboration Agreement (DUCA)
    Open grant
  • 2015 - 2016
    Test bed for wide-area software defined networking research (ARC LIEF project administered by The University of New South Wales)
    University of New South Wales
    Open grant
  • 2006 - 2008
    Generic Platform for Peer-to-peer Networks and Applications
    UQ New Staff Research Start-Up Fund
    Open grant

Availability

Professor Marius Portmann is:
Available for supervision

Looking for a supervisor? Read our advice on how to choose a supervisor.

Available projects

  • Machine Learning for Computer Networking

    Harness Machine Learning and AI techniques, with a focus on Large Language Models, for the configuration and management of Computer Networks.

Supervision history

Current supervision

  • Doctor Philosophy

    Low-energy LoRaWAN-based automatic and continuous measurement of organisational environmental performance.

    Principal Advisor

    Other advisors: Dr Siamak Layeghy, Professor Sara Dolnicar

  • Doctor Philosophy

    Towards Practical Machine Learning Based Network Intrusion Detection

    Principal Advisor

    Other advisors: Associate Professor Marcus Gallagher, Dr Siamak Layeghy

  • Doctor Philosophy

    Exploring the Capabilities of LoRaWAN IoT Technology for Multisensor Data Collection and Analysis

    Principal Advisor

    Other advisors: Dr Siamak Layeghy, Professor Sara Dolnicar

  • Doctor Philosophy

    eXtended Management Network System (xNMS)

    Principal Advisor

    Other advisors: Dr Siamak Layeghy

  • Doctor Philosophy

    Machine Learning for Improving Services and Management of Software Defined Networks

    Associate Advisor

    Other advisors: Dr Siamak Layeghy

  • Doctor Philosophy

    Enhancing the Privacy-Preserving ML techniques with Functional Encryption approach

    Associate Advisor

    Other advisors: Dr Siamak Layeghy

  • Doctor Philosophy

    Multi-Receiver Passive Radar using WirelessLAN for Indoor Localisation

    Associate Advisor

    Other advisors: Associate Professor Konstanty Bialkowski

  • Doctor Philosophy

    Enhancing Cyberbullying Detection in Arabic Social Media through Explainable AI and Natural Language Processing: A Human-Centric Approach

    Associate Advisor

    Other advisors: Dr Siamak Layeghy

Completed supervision

Enquiries

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