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In order to achieve non-invasive sensitive behavior surveillance, this article studies the mapping between time series data and sensitive behavior of applications (APPs). By triggering APP behavior, multivariate time series data of system state can be automatically collected through side channel scanning process, then data labeling, data slicing, and model training are carried out. After that, a sensitive behavior surveillance method based on time series classification through deep learning is explored. Experimental results show that compared to other baseline models, the proposed behavior surveillance based on convolutional neural networks (CNNs) exhibits performance advantages and robustness, where the overall accuracy of the test set in mixed behavior scenarios exceeds 70%.<\/jats:p>","DOI":"10.1177\/14727978241306022","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T03:15:24Z","timestamp":1745896524000},"page":"1854-1865","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["APP sensitive behavior surveillance method for smart terminals based on time series classification"],"prefix":"10.1177","volume":"25","author":[{"given":"Xingyuan","family":"Fan","sequence":"first","affiliation":[{"name":"Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, China"}]},{"given":"Ying","family":"Zhao","sequence":"additional","affiliation":[{"name":"Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, China"}]},{"given":"Jun","family":"Dong","sequence":"additional","affiliation":[{"name":"Guangzhou Power Supply Bureau, Guangdong Power Grid Co., Ltd., Guangzhou, China"}]},{"given":"Xiaodong","family":"Zhou","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou, China"},{"name":"Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou, China"}]},{"given":"Xiangyong","family":"Feng","sequence":"additional","affiliation":[{"name":"Electric Power Research Institute, China Southern Power Grid Co., Ltd., Guangzhou, China"},{"name":"Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid, Guangzhou, China"}]}],"member":"179","published-online":{"date-parts":[[2024,12,9]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TII.2017.2789219"},{"key":"e_1_3_2_3_2","unstructured":"VirtualApp. Virtual engine for android. 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