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)

21 - 40 of 172 works

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

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, 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

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

Before you email them, read our advice on how to contact a supervisor.

Supervision history

Current supervision

  • 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

    Learning-based optimisation strategies for sustainable IoT communications

    Principal Advisor

  • Doctor Philosophy

    Enhancing the Privacy-Preserving ML techniques with Functional Encryption approach

    Associate Advisor

    Other advisors: Dr Siamak Layeghy

Completed supervision

Enquiries

For media enquiries about Associate Professor Marius Portmann's areas of expertise, story ideas and help finding experts, contact our Media team:

communications@uq.edu.au