{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T19:02:15Z","timestamp":1763665335995,"version":"3.37.3"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,5]],"date-time":"2023-11-05T00:00:00Z","timestamp":1699142400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,5]],"date-time":"2023-11-05T00:00:00Z","timestamp":1699142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"International Cooperation and Exchange Program of National Natural Science Foundation of China","award":["62061136006"],"award-info":[{"award-number":["62061136006"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cybersecurity"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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 according to statistical features extract from the dataset. At first, 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 sensitive 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 L0 norm to achieve one-point attack.\u00a0We conduct experiments on several datasets to validate the effectiveness of this method.<\/jats:p>","DOI":"10.1186\/s42400-023-00179-4","type":"journal-article","created":{"date-parts":[[2023,11,5]],"date-time":"2023-11-05T02:01:31Z","timestamp":1699149691000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Attack based on data: a novel perspective to attack sensitive points directly"],"prefix":"10.1186","volume":"6","author":[{"given":"Yuyao","family":"Ge","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3720-0642","authenticated-orcid":false,"given":"Zhongguo","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lizhe","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiming","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengyang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"179_CR1","doi-asserted-by":"publisher","unstructured":"Abdelfattah SM, Abdelrahman GM, Wang M (2018) Augmenting the size of eeg datasets using generative adversarial networks. 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