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R. of China","award":["61962042"],"award-info":[{"award-number":["61962042"]}]},{"name":"University Youth Science and Technology Talent Development Project (Innovation Group Development Plan) of Inner Mongolia A. R. of China","award":["2022MS06020"],"award-info":[{"award-number":["2022MS06020"]}]},{"name":"University Youth Science and Technology Talent Development Project (Innovation Group Development Plan) of Inner Mongolia A. R. of China","award":["2022ZY0064"],"award-info":[{"award-number":["2022ZY0064"]}]},{"name":"University Youth Science and Technology Talent Development Project (Innovation Group Development Plan) of Inner Mongolia A. R. of China","award":["NMGIRT2318"],"award-info":[{"award-number":["NMGIRT2318"]}]},{"name":"Fund for Supporting the Reform and Development of Local Universities","award":["61962042"],"award-info":[{"award-number":["61962042"]}]},{"name":"Fund for Supporting the Reform and Development of Local Universities","award":["2022MS06020"],"award-info":[{"award-number":["2022MS06020"]}]},{"name":"Fund for Supporting the Reform and Development of Local Universities","award":["2022ZY0064"],"award-info":[{"award-number":["2022ZY0064"]}]},{"name":"Fund for Supporting the Reform and Development of Local Universities","award":["NMGIRT2318"],"award-info":[{"award-number":["NMGIRT2318"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>Connected devices in IoT systems usually have low computing and storage capacity and lack uniform standards and protocols, making them easy targets for cyberattacks. Implementing security measures like cryptographic authentication, access control, and firewalls for IoT devices is insufficient to fully address the inherent vulnerabilities and potential cyberattacks within the IoT environment. To improve the defensive capabilities of IoT systems, some research has focused on using deep learning techniques to provide new solutions for intrusion detection systems. However, some existing deep learning-based intrusion detection methods suffer from inadequate feature extraction and insufficient model generalization capability. To address the shortcomings of existing detection methods, we propose an intrusion detection model based on temporal convolutional residual modules. An attention mechanism is introduced to assess feature scores and enhance the model\u2019s ability to concentrate on critical features, thereby boosting its detection performance. We conducted extensive experiments on the ToN_IoT dataset and the UNSW-NB15 dataset, and the proposed model achieves accuracies of 99.55% and 89.23% on the ToN_IoT and UNSW-NB15 datasets, respectively, with improvements of 0.14% and 15.3% compared with the current state-of-the-art models. These results demonstrate the superior detection performance of the proposed model.<\/jats:p>","DOI":"10.3390\/fi16070255","type":"journal-article","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T16:51:12Z","timestamp":1721321472000},"page":"255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7578-9474","authenticated-orcid":false,"given":"Bo","family":"Cui","sequence":"first","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China"},{"name":"Engineering Research Center of Ecological Big Data, Ministry of Education, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yachao","family":"Chai","sequence":"additional","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China"},{"name":"Engineering Research Center of Ecological Big Data, Ministry of Education, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot 010021, China"},{"name":"Engineering Research Center of Ecological Big Data, Ministry of Education, Hohhot 010021, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5224-4048","authenticated-orcid":false,"given":"Keqin","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science, State University of New York, New Paltz, NY 12561, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MC.2017.62","article-title":"Botnets and internet of things security","volume":"50","author":"Bertino","year":"2017","journal-title":"Computer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1109\/MC.2017.201","article-title":"DDoS in the IoT: Mirai and other Botnets","volume":"50","author":"Kolias","year":"2017","journal-title":"Computer"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"65","DOI":"10.13052\/jcsm2245-1439.414","article-title":"Cyber security and the internet of things: Vulnerabilities, threats, intruders and attacks","volume":"4","author":"Abomhara","year":"2015","journal-title":"J. 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