On the Influence of Data Resampling for Deep Learning-Based Log Anomaly Detection : Insights and Recommendations
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
Original language | English |
---|---|
Pages (from-to) | 243-261 |
Journal / Publication | IEEE Transactions on Software Engineering |
Volume | 51 |
Issue number | 1 |
Online published | 9 Dec 2024 |
Publication status | Published - Jan 2025 |
Link(s)
Abstract
DLLAD methods may underperform in severely imbalanced datasets. Although data resampling has proven effective in other software engineering tasks, it has not been explored in LAD. This study aims to fill this gap by providing an in-depth analysis of the impact of diverse data resampling methods on existing DLLAD approaches from two distinct perspectives. Firstly, we assess the performance of these DLLAD approaches across four datasets with different levels of class imbalance, and we explore the impact of resampling ratios of normal to abnormal data on DLLAD approaches. Secondly, we evaluate the effectiveness of the data resampling methods when utilizing optimal resampling ratios of normal to abnormal data. Our findings indicate that oversampling methods generally outperform undersampling and hybrid sampling methods. Data resampling on raw data yields superior results compared to data resampling in the feature space. These improvements are attributed to the increased attention given to important tokens. By exploring the resampling ratio of normal to abnormal data, we suggest generating more data for minority classes through oversampling while removing less data from majority classes through undersampling. In conclusion, our study provides valuable insights into the intricate relationship between data resampling methods and DLLAD. By addressing the challenge of class imbalance, researchers and practitioners can enhance DLLAD performance. © 2024 IEEE.
Research Area(s)
- Class Imbalance, Data Resampling Methods, Deep Learning-Based Log Anomaly Detection, Empirical Analysis
Citation Format(s)
On the Influence of Data Resampling for Deep Learning-Based Log Anomaly Detection: Insights and Recommendations. / Ma, Xiaoxue; Zou, Huiqi; He, Pinjia et al.
In: IEEE Transactions on Software Engineering, Vol. 51, No. 1, 01.2025, p. 243-261.
In: IEEE Transactions on Software Engineering, Vol. 51, No. 1, 01.2025, p. 243-261.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review