Associate Professor Marius Portmann
Associate Professor

Marius Portmann

Email: 
Phone: 
+61 7 336 51636

Background

Dr Portmann's research interests are in the area of Computer Networks and Information Security.

Dr Portmann received his PhD in Electrical Engineering from the Swiss Federal Institute of Technology (ETH) in Zurich in 2003. His research interests are in overlay and Peer-to-peer networks and network security.

Employment History:

2013 - present Associate Professor ITEE/UQ

2009 – 2013 Senior Lecturer ITEE/UQ

2004 – 2008 Lecturer ITEE/UQ

2003 – 2004 Research Manager, School of Electrical Engineering and Telecommunications, UNSW

2002 – 2003 Senior Research Officer, School of Electrical Engineering and Telecommunications, UNSW

Availability

Associate 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

172 works between 2002 and 2024
All (172) Journal Article (53) Book Chapter (6) Conference Publication (99) Book (1) Other Outputs (13)

1 - 20 of 172 works

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

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

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

A configurable anonymisation approach for network flow data: Balancing utility and privacy

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

FlowTransformer: A transformer framework for flow-based network intrusion detection systems

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

Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasets

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

Feature extraction for machine learning-based intrusion detection in IoT networks

2023

Journal Article

Efficient block contrastive learning via parameter-free meta-node approximation

Kulatilleke, Gayan K., Portmann, Marius and Chandra, Shekhar S. (2023). Efficient block contrastive learning via parameter-free meta-node approximation. Neurocomputing, 561 126850. doi: 10.1016/j.neucom.2023.126850

Efficient block contrastive learning via parameter-free meta-node approximation

2023

Journal Article

Automatically monitoring environmental performance in tourism – The example of plate waste at all-you-can-eat buffets

Dolnicar, Sara, Gray, Angus, Grün, Bettina, Li, Hongwei and Portmann, Marius (2023). Automatically monitoring environmental performance in tourism – The example of plate waste at all-you-can-eat buffets. Annals of Tourism Research Empirical Insights, 4 (2) 100100, 1-3. doi: 10.1016/j.annale.2023.100100

Automatically monitoring environmental performance in tourism – The example of plate waste at all-you-can-eat buffets

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

Exploring Edge TPU for Network Intrusion Detection in IoT

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

HARBIC: Human activity recognition using bi-stream convolutional neural network with dual joint time-frequency representation

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

Exploring Edge TPU for deep feed-forward neural networks

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

XG-BoT: an explainable deep graph neural network for botnet detection and forensics

2023

Book

Self-powered Internet of Things : How energy harvesters can enable energy-positive sensing, processing, and communication

Sandhu, Muhammad Moid, Khalifa, Sara, Portmann, Marius and Jurdak, Raja (2023). Self-powered Internet of Things : How energy harvesters can enable energy-positive sensing, processing, and communication. Cham, Switzerland: Springer. doi: 10.1007/978-3-031-27685-9

Self-powered Internet of Things : How energy harvesters can enable energy-positive sensing, processing, and communication

2023

Journal Article

Light-Weight ML Aided Autonomous IoT Networks

Ghosh, Sushmita, Mandal, Akash Kumar, De, Swades, Chatterjee, Shouri and Portmann, Marius (2023). Light-Weight ML Aided Autonomous IoT Networks. IEEE Communications Magazine, 61 (6), 51-57. doi: 10.1109/mcom.001.2200539

Light-Weight ML Aided Autonomous IoT Networks

2023

Conference Publication

Energy aware smart sensing and implementation in green air pollution monitoring system

Ghosh, Sushmita, Das, Payali, Murugesh, Shakthipriya, De, Swades, Chatterjee, Shouri and Portmann, Marius (2023). Energy aware smart sensing and implementation in green air pollution monitoring system. IEEE International Conference on Communications (IEEE ICC), Rome, Italy, 28 May - 1 June 2023. New York, NY, United States: IEEE. doi: 10.1109/icc45041.2023.10279138

Energy aware smart sensing and implementation in green air pollution monitoring system

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

NF-ToN-IoT-v2

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

NF-UQ-NIDS-v2

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

NF-UNSW-NB15-v2

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

NF-UNSW-NB15

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

NF-BoT-IoT-v2

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

NF-UQ-NIDS

Current funding

  • 2021 - 2024
    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

Associate Professor Marius Portmann is:
Available for supervision

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Supervision history

Current supervision

  • Doctor Philosophy

    Learning-based optimisation strategies for sustainable IoT communications

    Principal Advisor

  • Doctor Philosophy

    Towards Autonomous Network Security

    Principal Advisor

    Other advisors: Associate Professor Marcus Gallagher, Dr Siamak Layeghy

  • 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

    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

    Enhancing the Privacy-Preserving ML techniques with Functional Encryption approach

    Associate Advisor

    Other advisors: Dr Siamak Layeghy

Completed supervision

Enquiries

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