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Improving intrusion detection through training data augmentation.

Otokwala, Uneneibotejit; Petrovski, Andrei; Kalutarage, Harsha

Authors



Contributors

Naghmeh Moradpoor
Editor

Atilla Elçi
Editor

Abstract

Imbalanced classes in datasets are common problems often found in security data. Therefore, several strategies like class resampling and cost-sensitive training have been proposed to address it. In this paper, we propose a data augmentation strategy to oversample the minority classes in the dataset. Using our Sort-Augment-Combine (SAC) technique, we split the dataset into subsets of the class labels and then generate synthetic data from each of the subsets. The synthetic data were then used to oversample the minority classes. Upon the completion of the oversampling, the independent classes were combined to form an augmented training data for model fitting. Using performance metrics such as accuracy, recall (sensitivity) and true positives (specificity), the models trained using the augmented datasets show an improvement in performance metrics over the original dataset. Similarly, in a binary class dataset, SAC performed optimally and the combination of SAC and ROSE model shows an improvement in overall accuracy, sensitivity and specificity when compared with the performance of the Random Forest model on the original dataset, ROSE and SMOTE augmented datasets.

Citation

OTOKWALA, U., PETROVSKI, A. and KALUTARAGE, H. 2021. Improving intrusion detection through training data augmentation. In Moradpoor, N., El├ži, A. and Petrovski, A. (eds.) Proceedings of 14th International conference on Security of information and networks 2021 (SIN 2021), 15-17 December 2021, [virtual conference]. Piscataway: IEEE [online], article 17. Available from: https://doi.org/10.1109/SIN54109.2021.9699293

Conference Name 14th International conference on Security of information and networks 2021 (SIN 2021)
Conference Location [virtual conference]
Start Date Dec 15, 2021
End Date Dec 17, 2021
Acceptance Date Dec 7, 2021
Online Publication Date Dec 17, 2021
Publication Date Feb 10, 2022
Deposit Date Feb 11, 2022
Publicly Available Date Feb 11, 2022
Publisher IEEE Institute of Electrical and Electronics Engineers
Book Title Proceedings of the 14th International conference on Security of information and networks 2021 (SIN 2021)
ISBN 9781728192666
DOI https://doi.org/10.1109/SIN54109.2021.9699293
Keywords Imbalanced data; Minority oversampling; Data augmentation; Intrusion detection
Public URL https://rgu-repository.worktribe.com/output/1592322

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