All Outputs (2)

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..

Enhancing intrusion detection through data perturbation augmentation strategy. (2024)
Presentation / Conference Contribution
OTOKWALA, U.J., PETROVSKIY, A.V. and KOTENKO, I.V. 2024. Enhancing intrusion detection through data perturbation augmentation strategy. In Proceedings of the 2024 IEEE (Institute of Electrical and Electronics Engineers) Ural-Siberian conference on biomedical engineering, radioelectronics and information technology (USBEREIT 2024), 13-15 May 2024, Yekaterinburg, Russia. Piscataway: IEEE [online], pages 269-272. Available from: https://doi.org/10.1109/USBEREIT61901.2024.10584007

Intrusion data augmentation is an approach used to increase the size of the training data sample to improve the classification capabilities of machine-learning algorithms applied to intrusion detection. In this study, we introduced data perturbation... Read More about Enhancing intrusion detection through data perturbation augmentation strategy..