All Outputs (4)

Temporal graph convolutional autoencoder based fault detection for renewable energy applications. (2024)
Presentation / Conference Contribution
ARIFEEN, M. and PETROVSKI, A. 2024. Temporal graph convolutional autoencoder based fault detection for renewable energy applications. In Proceedings of the 7th IEEE (Institute of Electrical and Electronics Engineers) Industrial cyber-physical systems international conference 2024 (ICPS 2024), 12-15 May 2024, St. Louis, USA. Piscataway: IEEE [online], article number 10639998. Available from: https://doi.org/10.1109/ICPS59941.2024.10639998

Detecting faults in energy generation systems is a challenging task due to the complex nature of the system, measurement noise, and outliers. Recently, researchers have shown an increasing interest in using data-driven models that utilize sensor data... Read More about Temporal graph convolutional autoencoder based fault detection for renewable energy applications..

HEADS: hybrid ensemble anomaly detection system for Internet-of-Things networks. (2024)
Presentation / Conference Contribution
AHMAD, Z., PETROVSKI, A., ARIFEEN, M., KHAN, A.S. and SHAH, S.A. 2024. HEADS: hybrid ensemble anomaly detection system for Internet-of-Things networks. In Iliadis, L., Maglogiannis, I., Papaleonidas, A., Pimenidis, E. and Jayne, C. (eds.) Engineering applications on neural networks: proceedings of the 25th International Engineering applications on neural networks 2024 (EANN 2024), 27-30 June 2024, Corfu, Greece. Communications in computer and information science, 2141. Cham: Springer [online], pages 178-190. Available from: https://doi.org/10.1007/978-3-031-62495-7_14

The rapid expansion of Internet-of-Things (IoT) devices has revolutionized connectivity, facilitating the exchange of extensive data within IoT networks via the traditional internet. However, this innovation has also increased security concerns due t... Read More about HEADS: hybrid ensemble anomaly detection system for Internet-of-Things networks..

Enhancing gas-pipeline monitoring with graph neural networks: a new approach for acoustic emission analysis under variable pressure conditions. (2024)
Presentation / Conference Contribution
HASAN, M.J., ARIFEEN, M., SOHAIB, M., ROHAN, A. and KANNAN, S. 2024. Enhancing gas pipeline monitoring with graph neural networks: a new approach for acoustic emission analysis under variable pressure conditions. To be published in Proceedings of the 20th International conference on condition monitoring and asset management 2024 (CM 2024), 18-20 June 2024, Oxford, UK. Northampton: BINDT [online], (accepted). To be made available at: https://doi.org/10.1784/cm2024.4b3

Traditional machine learning (ML) and deep learning (DL)-based acoustic emission (AE) data-driven condition monitoring models face several reliability issues due to factors such as fluid pressure changes, flange vibrations, inconsistent leak lengths,... Read More about Enhancing gas-pipeline monitoring with graph neural networks: a new approach for acoustic emission analysis under variable pressure conditions..

A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things. (2024)
Presentation / Conference Contribution
OTOKWALA, U., ARIFEEN, M. and PETROVSKI, A. 2024. A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things. In Panoutsos, G., Mihaylova, L.S. and Mahfouf, M. (eds.) Advances in computational intelligence systems: contributions presented at the 21st UK workshop on computational intelligence (UKCCI 2022), 7-9 September 2022, Sheffield, UK. Advances in intelligent systems and computing, 1454. Cham: Springer [online], pages 238-249. Available from: https://doi.org/10.1007/978-3-031-55568-8_20

The detection of zero-day attacks in the IoT network is a challenging task due to unknown security vulnerabilities. Also, the unavailability of the data makes it difficult to train a machine learning (ML) model about new vulnerabilities. The existing... Read More about A comparative study of novelty detection models for zero day intrusion detection in industrial Internet of Things..