@inproceedings{
han2024adaid,
title={Ada-iD: Active Domain Adaption for Intrusion Detection},
author={Fujun Han and Peng Ye and Shukai Duan and Lidan Wang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=Hb0i9E6acF}
}
Vision-based intrusion detection has many applications in life environments, e.g., security, intelligent monitoring, and autonomous driving. Previous works improve the performance of intrusion detection under unknown environments by introducing unsupervised domain adaption (UDA) methods. However, these works do not fully fulfill the practical requirements due to the performance gap between UDA and fully supervised methods. To address the problem, we develop a new and vital active domain adaption intrusion detection task, namely ADA-ID. Our aim is to query and annotate the most informative samples of the target domain at the lowest possible cost, striving for a balance between achieving high performance and keeping low annotation expenses. Specifically, we propose a multi-task joint active domain adaption intrusion detection framework, namely ADAID-YOLO. It consists of a lower branch for detection and an upper branch for segmentation. Further, three effective strategies are designed to better achieve the ADA-ID task: 1) An efficient Dynamic Diffusion Pseudo-Labeling method (DDPL) is introduced to get Pseudo ground truth to help identify areas of uncertainty in segmentation. 2) A Enhanced Region Impurity and Prediction Uncertainty sampling strategy (Enhanced-RIPU) is proposed to better capture the uncertainty of the segmentation region. 3) A Multi-Element Joint sampling strategy (MEJ) is designed to calculate the uncertainty of the detection comprehensively. Finally, comprehensive experiments and comparisons are conducted on multiple dominant intrusion detection datasets. The results show that our method can outperform other classic and promising active domain adaption methods and reach current SOTA performance, even surpassing the performance of UDA and full supervision on Normal→Foggy with only 0.1% and 10% data annotation, respectively. All the source codes, and trained models will be public.
Ada-iD: Active Domain Adaption for Intrusion Detection
Fujun Han Peng Ye Shukai Duan Lidan Wang