Towards Understanding the Robustness Against Evasion Attack on Categorical Data
Authors: Hongyan Bao, Yufei Han, Yujun Zhou, Yun Shen, Xiangliang Zhang
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Corroborating these theoretical findings with a substantial experimental study over various real-world categorical datasets, we can empirically assess the impact of the key adversarial risk factors over a targeted learning system with categorical inputs." and "4 EXPERIMENTAL STUDY We instantiate the study with standard LSTM based classifiers trained on three multi-class datasets collected from real-world applications of Text analysis, Cyber security and Biomedicine. |
| Researcher Affiliation | Collaboration | Hongyan Bao King Abdullah University of Science and Technology hongyan.bao@kaust.edu.sa Yufei Han INRIA yufei.han@inria.fr Yujun Zhou King Abdullah University of Science and Technology yujun.zhou@kaust.edu.sa Yun Shen Net App yun.shen@netapp.com Xiangliang Zhang University of Notre Dame xzhang33@nd.edu" and "The author contributed to this work while at Norton Life Lock. |
| Pseudocode | Yes | We give the pseudo codes of FSGS and Rand GS in Algorithm.1 and 2 in Appendix.D." and "The pseudo-codes of OMPGS is presented in Algorithm 3, which explains how it is adopted to solve Eq.4 and Eq.5. |
| Open Source Code | Yes | Implementations are available at https://github.com/XYZ211923Y/-Robust XXXXX. |
| Open Datasets | Yes | Yelp-5 (Yelp)(Asghar, 2016).", "Intrusion Prevention System Dataset (IPS) (Wang et al., 2020).", "Splice-junction Gene Sequences (Splice) (Noordewier et al., 1991).", "Electronic Health Records (EHR) (Ma et al., 2018). |
| Dataset Splits | No | We randomly select 80% of each dataset for training and others for testing." and "We split randomly each dataset into two non-overlapped subsets: 80% of them are used for training and the left 20% form a testing set." The paper explicitly states a training and testing split but does not specify a separate validation split. |
| Hardware Specification | Yes | We implement the empirical study using the Python library Py Torch and conduct all the experiments on Linux server with 2 GPUs (Ge Force 1080Ti) and 16-core CPU (Intel Xeon). |
| Software Dependencies | No | We implement the empirical study using the Python library Py Torch". The paper mentions PyTorch but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The LSTM-based classifiers with Re Lu activation function and dropout achieve accuracy scores of 0.61, 0.92 and 0.95 respectively for Yelp, IPS and Splice." and "The tolerance threshold Γ is tested on 0.4 and 0 to assess our proposed assessment method with varied tolerance to adversarial threats in safety-critical applications." and "For Rand GS and OMPGS, we empirically set the number of candidate attributes in each iteration of greedy search to be 10 globally for all the datasets. |