Summary of Towards Trustworthy Web Attack Detection: An Uncertainty-aware Ensemble Deep Kernel Learning Model, by Yonghang Zhou et al.
Towards Trustworthy Web Attack Detection: An Uncertainty-Aware Ensemble Deep Kernel Learning Model
by Yonghang Zhou, Hongyi Zhu, Yidong Chai, Yuanchun Jiang, Yezheng Liu
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an Uncertainty-aware Ensemble Deep Kernel Learning (UEDKL) model for detecting web attacks from HTTP request payload data. The model captures model uncertainty from both the data distribution and model parameters perspectives. A deep kernel learning model distinguishes normal requests from different types of attacks, while multiple models are trained as base learners to estimate model uncertainty from the perspective of model parameters. An attention-based ensemble approach integrates predictions and model uncertainty. The framework is evaluated on BDCI and SRBH datasets, demonstrating significant improvement in both detection performance and uncertainty estimation quality compared to benchmark models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to detect web attacks using a special type of machine learning called deep kernel learning. This method helps by showing how sure the model is about its predictions. The researchers trained multiple models on different data sets and combined them to make better decisions. They also came up with a new way to measure how good their approach was. The results show that this new method works much better than other approaches. |
Keywords
* Artificial intelligence * Attention * Machine learning
