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Due to good performance of deep learning methods, many devices integrated well-trained models in them. IoT empowers users to communicate and control physical devices to achieve vital information. However, these models are vulnerable to adversarial attacks, which largely bring potential risks to the normal application of deep learning methods. For instance, very little changes even one point in the IoT time-series data could lead to unreliable or wrong decisions. Moreover, these changes could be deliberately generated by following an adversarial attack strategy. We propose a robust IoT data classification model based on an encode-decode joint training model. Furthermore, thermometer encoding is taken as a nonlinear transformation to the original training examples that are used to reconstruct original time series examples through the encode-decode model. The trained ResNet model based on reconstruction examples is more robust to the adversarial attack. Experiments show that the trained model can successfully resist to fast gradient sign method attack to some extent and improve the security of the time series data classification model.<\/jats:p>","DOI":"10.1155\/2021\/5537041","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T19:20:17Z","timestamp":1615404017000},"page":"1-11","source":"Crossref","is-referenced-by-count":9,"title":["An IoT Time Series Data Security Model for Adversarial Attack Based on Thermometer Encoding"],"prefix":"10.1155","volume":"2021","author":[{"given":"Zhongguo","family":"Yang","sequence":"first","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1813-1415","authenticated-orcid":true,"given":"Irshad Ahmed","family":"Abbasi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Science and Arts at Belgarn, University of Bisha, Sabt Al-Alaya 61985, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3895-2309","authenticated-orcid":true,"given":"Fahad","family":"Algarni","sequence":"additional","affiliation":[{"name":"College of Computing and Information Technology, Faculty of Computing and Information Technology, University of Bisha, Bisha 61922, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2753-8615","authenticated-orcid":true,"given":"Sikandar","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Technology, China University of Petroleum, Beijing 102249, China"},{"name":"Beijing Key Laboratory of Petroleum Data Mining, China University of Petroleum-Beijing, Beijing 102249, China"}]},{"given":"Mingzhu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, North China University of Technology, Beijing, China"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.07.060"},{"key":"2","first-page":"1","article-title":"Augmenting the size of EEG datasets using generative adversarial networks","author":"S. 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Agnieszka Nawrocka","year":"2013"},{"key":"8","article-title":"Adversarial attacks on deep neural networks for time series classification","author":"H. I. Fawaz","year":"2019"},{"key":"9","first-page":"1578","article-title":"Time series classification from scratch with deep neural networks: a strong baseline","author":"Z. Wang"},{"issue":"9","key":"10","first-page":"1","article-title":"Adversarial examples: attacks and defenses for deep learning","volume":"30","author":"X. Yuan","year":"2019","journal-title":"IEEE Transactions on Neural Networks"},{"key":"11","first-page":"1625","article-title":"Robust physical-world attacks on deep learning visual classification","author":"K. Eykholt"},{"key":"12","article-title":"Explaining and harnessing adversarial examples","author":"I. J. Goodfellow"},{"key":"13","doi-asserted-by":"crossref","article-title":"Adversarial examples in the physical world","author":"A. Kurakin","DOI":"10.1201\/9781351251389-8"},{"key":"14","first-page":"582","article-title":"Distillation as a defense to adversarial perturbations against deep neural networks","author":"N. Papernot"},{"key":"15","article-title":"A study of the effect of JPG compression on adversarial images","author":"G. K. Dziugaite"},{"key":"16","article-title":"Towards deep neural network architectures robust to adversarial examples","author":"S. Gu"},{"key":"17","article-title":"Adversarial examples for semantic segmentation and object detection","author":"C. Xie"},{"key":"18","first-page":"1660","article-title":"Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients","author":"A. S. Ross"},{"key":"19","article-title":"Thermometer encoding: one hot way to resist adversarial examples","author":"J. 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