Under adversarial attacks, time series regression and classification are vulnerable. Adversarial defense, on the other hand, can make the models more resilient. It is important to evaluate how vulnerable different time series models are to attacks and how well they recover using defense. The sensitivity to various attacks and the robustness using the defense of several time series models are investigated in this study. Experiments are run on seven-time series models with three adversarial attacks and one adversarial defense. According to the findings, all models, particularly GRU and RNN, appear to be vulnerable. LSTM and GRU also have better defense recovery. FGSM exceeds the competitors in terms of attacks. PGD attacks are more difficult to recover from than other sorts of attacks.
翻译:在对抗性攻击中,时间序列回归和分类是脆弱的。相反,反向防御可以使模型更具弹性。重要的是要评估不同的时间序列模型对攻击的脆弱程度以及它们利用防御恢复的好坏。本研究调查了对各种攻击的敏感性以及使用若干时间序列模型的防御的稳健性。实验以7次系列模型进行,有3次对抗性攻击和1次对抗性防御。根据调查结果,所有模型,特别是GRU和RNN,似乎都比较脆弱。LSTM和GRU也有更好的防御恢复能力。FGSM在攻击方面超过了竞争对手。PGD攻击比其他类型的攻击更难恢复。