The University of Queensland

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

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

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

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

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

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

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

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

SCOR: a constraint programming approach to software defined networking

Layeghy, Siamak and Portmann, Marius (2022). SCOR: a constraint programming approach to software defined networking. Horizons in computer science research. Volume 22. (pp. 141-191) edited by Thomas S. Clary. New York, NY United States: Nova Science Publishers.

NetFlow datasets for machine learning-based network intrusion detection systems

Sarhan, Mohanad, Layeghy, Siamak, Moustafa, Nour and Portmann, Marius (2021). NetFlow datasets for machine learning-based network intrusion detection systems. 10th EAI International Conference, BDTA 2020 and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, 11 December 2020. Cham, Switzerland: Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-3-030-72802-1_9

SCOR: a constraint programming approach to software defined networking

Layeghy, Siamak and Portmann, Marius (2022). SCOR: a constraint programming approach to software defined networking. Horizons in computer science research. Volume 22. (pp. 141-191) edited by Thomas S. Clary. New York, NY United States: Nova Science Publishers.

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

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

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

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

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

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

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

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

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, 100749. doi: 10.1016/j.iot.2023.100749

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

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

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

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

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

Sarhan, Mohanad, Layeghy, Siamak, Moustafa, Nour, Gallagher, Marcus and Portmann, Marius (2022). Feature extraction for machine learning-based intrusion detection in IoT networks. Digital Communications and Networks. doi: 10.1016/j.dcan.2022.08.012

Towards a standard feature set for network intrusion detection system datasets

Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2021). Towards a standard feature set for network intrusion detection system datasets. Mobile Networks and Applications, 27 (1), 357-370. doi: 10.1007/s11036-021-01843-0

Automatic fetal movement recognition from multi-channel accelerometry data

Mesbah, Mostefa, Khlif, Mohamed Salah, Layeghy, Siamak, East, Christine E., Dong, Shiying, Brodtmann, Amy, Colditz, Paul B. and Boashash, Boualem (2021). Automatic fetal movement recognition from multi-channel accelerometry data. Computer Methods and Programs in Biomedicine, 210 106377, 106377. doi: 10.1016/j.cmpb.2021.106377

Deep learning-based cattle behaviour classification using joint time-frequency data representation

Hosseininoorbin, Seyedehfaezeh, Layeghy, Siamak, Kusy, Brano, Jurdak, Raja, Bishop-Hurley, Greg J., Greenwood, Paul L and Portmann, Marius (2021). Deep learning-based cattle behaviour classification using joint time-frequency data representation. Computers and Electronics in Agriculture, 187 106241, 106241. doi: 10.1016/j.compag.2021.106241

P-SCOR: integration of constraint programming orchestration and programmable data plane

Melis, Andrea, Layeghy, Siamak, Berardi, Davide, Portmann, Marius, Prandini, Marco and Callegati, Franco (2020). P-SCOR: integration of constraint programming orchestration and programmable data plane. IEEE Transactions on Network and Service Management, 18 (1) 9311177, 1-1. doi: 10.1109/tnsm.2020.3048277

Flow-level load balancing of HTTP traffic using open flow

Al-Najjar, Anees, Layeghy, Siamak, Portmann, Marius and Indulska, Jadwiga (2018). Flow-level load balancing of HTTP traffic using open flow. Australian Journal of Telecommunications and the Digital Economy, 6 (4), 75-95. doi: 10.18080/ajtde.v6n4.166

A new QoS routing northbound interface for SDN

Layeghy, Siamak, Pakzad, Farzaneh and Portmann, Marius (2017). A new QoS routing northbound interface for SDN. Australian Journal of Telecommunications and the Digital Economy, 5 (1), 92-115. doi: 10.18080/ajtde.v5n1.91

Neonatal EEG at scalp is focal and implies high skull conductivity in realistic neonatal head models

Odabaee, Maryam, Tokariev, Anton, Layeghy, Siamak, Mesbah, Mostefa, Colditz, Paul B., Ramon, Ceon and Vanhatalo, Sampsa (2014). Neonatal EEG at scalp is focal and implies high skull conductivity in realistic neonatal head models. NeuroImage, 96, 73-80. doi: 10.1016/j.neuroimage.2014.04.007

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

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

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

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

FlowGAN - Synthetic Network Flow Generation using Generative Adversarial Networks

Manocchio, Liam Daly, Layeghy, Siamak and Portmann, Marius (2021). FlowGAN - Synthetic Network Flow Generation using Generative Adversarial Networks. International Conference on Computational Science and Engineering (CSE), Shenyang, China, 20-22 October 2021. Piscataway, NJ, United States: IEEE. doi: 10.1109/cse53436.2021.00033

Scaling Spectrogram Data Representation for Deep Learning on Edge TPU

Hosseininoorbin, Seyedehfaezeh, Layeghy, Siamak, Kusy, Brano, Jurdak, Raja and Portmann, Marius (2021). Scaling Spectrogram Data Representation for Deep Learning on Edge TPU. 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), Kassel, Germany, 22-26 March 2021. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers Inc.. doi: 10.1109/PerComWorkshops51409.2021.9431041

NetFlow datasets for machine learning-based network intrusion detection systems

Sarhan, Mohanad, Layeghy, Siamak, Moustafa, Nour and Portmann, Marius (2021). NetFlow datasets for machine learning-based network intrusion detection systems. 10th EAI International Conference, BDTA 2020 and 13th EAI International Conference on Wireless Internet, WiCON 2020, Virtual Event, 11 December 2020. Cham, Switzerland: Springer Science and Business Media Deutschland GmbH. doi: 10.1007/978-3-030-72802-1_9

Enhancing quality of experience of VoIP traffic in SDN based end-hosts

Al-Najjar, Anees, Layeghy, Siamak, Portmann, Marius and Indulska, Jadwiga (2019). Enhancing quality of experience of VoIP traffic in SDN based end-hosts. 28th International Telecommunication Networks and Applications Conference, ITNAC 2018, Sydney, NSW Australia, 21-23 November 2018. New York, NY USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/ATNAC.2018.8615286

Evaluation of Mininet-WiFi integration via ns-3

Pakzad, Farzaneh, Layeghy, Siamak and Portmann, Marius (2017). Evaluation of Mininet-WiFi integration via ns-3. 26th International Telecommunication Networks and Applications Conference, ITNAC 2016, Dunedin, New Zealand, 7 - 9 December 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ATNAC.2016.7878816

Experimental evaluation of the impact of DoS attacks in SDN

Alharbi, Talal, Layeghy, Siamak and Portmann, Marius (2017). Experimental evaluation of the impact of DoS attacks in SDN. 27th International Telecommunication Networks and Applications Conference (ITNAC), Melbourne, Australia, 22-24 November 2017. Piscataway, NJ, United States: IEEE.

Link capacity estimation in SDN-based end-hosts

Al-Najjar, Anees, Pakzad, Farzaneh, Layeghy, Siamak and Portmann, Marius (2017). Link capacity estimation in SDN-based end-hosts. 10th International Conference on Signal Processing and Communication Systems, ICSPCS 2016, Surfers Paradise, QLD, Australia, 19 - 21 December 2016. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICSPCS.2016.7843372

Pushing SDN to the end-host, network load balancing using OpenFlow

Al-Najjar, Anees, Layeghy, Siamak and Portmann, Marius (2016). Pushing SDN to the end-host, network load balancing using OpenFlow. 13th IEEE International Conference on Pervasive Computing and Communication Workshops, PerCom Workshops 2016, Sydney, NSW, Australia, 14-18 March 2016. NEW YORK: Institute of Electrical and Electronics Engineers. doi: 10.1109/PERCOMW.2016.7457129

SCOR: constraint programming based northbound interface for SDN

Layeghy, Siamak, Pakzad, Farzaneh and Portmann, Marius (2016). SCOR: constraint programming based northbound interface for SDN. International Telecommunication Networks and Applications Conference, ITNAC, Dunedin, New Zealand, 7-9 December 2016. Piscataway, NJ, United States: IEEE. doi: 10.1109/ATNAC.2016.7878788

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

Non-invasive monitoring of fetal movements using time-frequency features of accelerometry

Layeghy, Siamak, Azemi, Ghasem, Colditz, Paul and Boashash, Boualem (2014). Non-invasive monitoring of fetal movements using time-frequency features of accelerometry. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2014), Florence, Italy, 4-9 May 2014. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICASSP.2014.6854429

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

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

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

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

CIC-BoT-IoT

Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marus (2023). CIC-BoT-IoT. The University of Queensland. (Dataset) doi: 10.48610/c80fccd

CIC-ToN-IoT

Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). CIC-ToN-IoT. The University of Queensland. (Dataset) doi: 10.48610/f6884ce

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-v2

Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-BoT-IoT-v2. The University of Queensland. (Dataset) doi: 10.48610/ec73920

NF-ToN-IoT

Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-ToN-IoT. The University of Queensland. (Dataset) doi: 10.48610/2fa2ed6

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-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-v2

Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-UNSW-NB15-v2. The University of Queensland. (Dataset) doi: 10.48610/ffbb0c1

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-v2

Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-UQ-NIDS-v2. The University of Queensland. (Dataset) doi: 10.48610/631a24a

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-v2

Sarhan, Mohanad, Layeghy, Siamak and Portmann, Marius (2023). NF-CSE-CIC-IDS2018-v2. The University of Queensland. (Dataset) doi: 10.48610/e9636b7

SCOR: Software-defined Constrained Optimal Routing Platform for SDN

Layeghy, Siamak (2018). SCOR: Software-defined Constrained Optimal Routing Platform for SDN. PhD Thesis, School of Information Technology and Electrical Engineering, The University of Queensland. doi: 10.14264/uql.2018.820