Attack based on data: a novel perspective to attack sensitive points directly  

作　　者：Yuyao Ge Zhongguo Yang Lizhe Chen Yiming Wang Chengyang Li 

机构地区：[1]School of Information Science and Technology,North China University of Technology,Beijing,China [2]Bejing Key Laboratory on Integration and Analysis of Large-Scale Stream Data,North China University of Technology,Beijing,China [3]School of Computer Science,Peking University,Beijing,China

出　　处：《Cybersecurity》2024年第3期111-123,共13页网络空间安全科学与技术（英文）

基　　金：This work is supported by the Key Program of National Natural Science Foundation of China(No.61832004);International Cooperation and Exchange Program of National Natural Science Foundation of China(Grant no.62061136006).

摘　　要：Adversarial attack for time-series classification model is widely explored and many attack methods are proposed.But there is not a method of attack based on the data itself.In this paper,we innovatively proposed a black-box sparse attack method based on data location.Our method directly attack the sensitive points in the time-series data accord-ing to statistical features extract from the dataset.At frst,we have validated the transferability of sensitive points among DNNs with different structures.Secondly,we use the statistical features extract from the dataset and the sensi-tive rate of each point as the training set to train the predictive model.Then,predicting the sensitive rate of test set by predictive model.Finally,perturbing according to the sensitive rate.The attack is limited by constraining the LO norm to achieve one-point attack.We conduct experiments on several datasets to validate the effectiveness of this method.

关 键 词：Black-box adversarial attack Time series classification Data mining 

分 类 号：TP393.08[自动化与计算机技术—计算机应用技术]