{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:39:40Z","timestamp":1774967980976,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819755875","type":"print"},{"value":"9789819755882","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-97-5588-2_2","type":"book-chapter","created":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T18:02:48Z","timestamp":1723485768000},"page":"13-23","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Neural Network-Based Intrusion Detection in Internet of Things: A State-of-the-Art Review"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2160-6418","authenticated-orcid":false,"given":"Zhiqi","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9139-2892","authenticated-orcid":false,"given":"Weidong","family":"Fang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8041-0197","authenticated-orcid":false,"given":"Chunsheng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhiwei","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinhang","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5084-3351","authenticated-orcid":false,"given":"Wuxiong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,13]]},"reference":[{"issue":"6","key":"2_CR1","doi-asserted-by":"publisher","first-page":"4131","DOI":"10.1109\/TII.2020.3006137","volume":"17","author":"W Fang","year":"2021","unstructured":"Fang, W., Cui, N., Chen, W., Zhang, W., Chen, Y.: A trust-based security system for data collection in smart city. IEEE Trans. Ind. Inf. 17(6), 4131\u20134140 (2021)","journal-title":"IEEE Trans. Ind. Inf."},{"key":"2_CR2","doi-asserted-by":"publisher","first-page":"88116","DOI":"10.1109\/ACCESS.2023.3306452","volume":"11","author":"Z Li","year":"2023","unstructured":"Li, Z., Fang, W., Zhu, C., Gao, Z., Zhang, W.: AI-enabled trust in distributed networks. IEEE Access 11, 88116\u201388134 (2023)","journal-title":"IEEE Access"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Fang, W., Zhu, C., Guizani, M., Rodrigues, J.J.P.C., Zhang, W.: HC-TUS: human cognition-based trust update scheme for AI-enabled VANET. IEEE Netw. https:\/\/doi.org\/10.1109\/MNET.2023.3320934","DOI":"10.1109\/MNET.2023.3320934"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Yadav, N., Pande, S., Khamparia, A., Gupta, D.: Intrusion detection system on IoT with 5G network using deep learning. Wirel. Commun. Mob. Comput. 2022, Article no. 9304689 (2022)","DOI":"10.1155\/2022\/9304689"},{"key":"2_CR5","doi-asserted-by":"publisher","first-page":"121173","DOI":"10.1109\/ACCESS.2022.3220622","volume":"10","author":"PLS Jayalaxmi","year":"2022","unstructured":"Jayalaxmi, P.L.S., Saha, R., Kumar, G., Conti, M., Kim, T.H.: Machine and deep learning solutions for intrusion detection and prevention in IoTs: a survey. IEEE Access 10, 121173\u2013121192 (2022)","journal-title":"IEEE Access"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Khan, A.R., Kashif, M., Jhaveri, R.H., Raut, R., Saba, T., Bahaj, S.A.: Deep learning for intrusion detection and security of Internet of Things (IoT): current analysis, challenges, and possible solutions. Secur. Commun. Netw. 2022 Article no. 4016073 (2022)","DOI":"10.1155\/2022\/4016073"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Adi, L.W.P., Mandala, S., Nugraha, Y.: DDoS attack detection system using neural network on Internet of Things. In: 2022 International Conference on Data Science and Its Applications (ICoDSA), Bandung, Indonesia, pp. 41\u201346. IEEE (2022)","DOI":"10.1109\/ICoDSA55874.2022.9862848"},{"key":"2_CR8","doi-asserted-by":"crossref","unstructured":"Ravi Kiran Varma, P., Sathiya, R.R., Vanitha, M.: Enhanced Elman spike neural network based intrusion attack detection in software defined Internet of Things network. Concur. Comput. Pract. Exp. 35(2), Article no. e7503 (2023)","DOI":"10.1002\/cpe.7503"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Firas Mohammed Aswad, F.M.A., Ali Mohammed Saleh Ahmed, A.M.S.A., Nafea Ali Majeed Alhammadi, N.A.M.A., Bashar Ahmad Khalaf, B.A.K., Mostafa, S.A., Mostafa, S.A.: Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks. J. Intell. Syst. 32, 1\u201313 (2023)","DOI":"10.1515\/jisys-2022-0155"},{"key":"2_CR10","doi-asserted-by":"crossref","unstructured":"Yousuf, O., Mir, R.N.: DDoS attack detection in Internet of Things using recurrent neural network. Comput. Electr. Eng. 101, Article no. 108034 (2022)","DOI":"10.1016\/j.compeleceng.2022.108034"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Reddy, D.K., Behera, H.S., Nayak, J., Vijayakumar, P., Naik, B., Singh, P.K.: Deep neural network based anomaly detection in Internet of Things network traffic tracking for the applications of future smart cities. Trans. Emerg. Telecommun. Technol. 32(7), Article no. e4121 (2021)","DOI":"10.1002\/ett.4121"},{"key":"2_CR12","doi-asserted-by":"publisher","first-page":"89337","DOI":"10.1109\/ACCESS.2020.2994079","volume":"8","author":"S Latif","year":"2020","unstructured":"Latif, S., Zou, Z., Idrees, Z., Ahmad, J.: A novel attack detection scheme for the industrial Internet of Things using a lightweight random neural network. IEEE Access 8, 89337\u201389350 (2020)","journal-title":"IEEE Access"},{"key":"2_CR13","doi-asserted-by":"publisher","first-page":"55595","DOI":"10.1109\/ACCESS.2021.3071766","volume":"9","author":"ZE Huma","year":"2021","unstructured":"Huma, Z.E., Latif, S., Ahmad, J., et al.: A hybrid deep random neural network for cyberattack detection in the industrial Internet of Things. IEEE Access 9, 55595\u201355605 (2021)","journal-title":"IEEE Access"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Le, K.H., Nguyen, M.H., Tran, T.D., Tran, N.D.: IMIDS: an intelligent intrusion detection system against cyber threats in IoT. Electronics 11(4), Article no. 524 (2022)","DOI":"10.3390\/electronics11040524"},{"issue":"6","key":"2_CR15","doi-asserted-by":"publisher","first-page":"4265","DOI":"10.1109\/TII.2021.3122370","volume":"18","author":"W Fang","year":"2022","unstructured":"Fang, W., Zhu, C., Yu, F.R., Wang, K., Zhang, W.: Towards energy-efficient and secure data transmission in AI-enabled software defined industrial networks. IEEE Trans. Ind. Inf. 18(6), 4265\u20134274 (2022)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"1","key":"2_CR16","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1109\/JIOT.2023.3295438","volume":"11","author":"W Fang","year":"2024","unstructured":"Fang, W., Zhu, C., Zhang, W.: Toward secure and lightweight data transmission for cloud\u2013edge\u2013terminal collaboration in artificial intelligence of things. IEEE Internet Things J. 11(1), 105\u2013113 (2024)","journal-title":"IEEE Internet Things J."},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Parra, G.D.L.T., Rad, P., Choo, K.K.R., Beebe, N.: Detecting Internet of Things attacks using distributed deep learning. J. Netw. Comput. Appl. 163, Article no. 102662 (2020)","DOI":"10.1016\/j.jnca.2020.102662"},{"issue":"12","key":"2_CR18","doi-asserted-by":"publisher","first-page":"9960","DOI":"10.1109\/JIOT.2021.3119055","volume":"9","author":"R Zhao","year":"2021","unstructured":"Zhao, R., et al.: A novel intrusion detection method based on lightweight neural network for Internet of Things. IEEE Internet Things J. 9(12), 9960\u20139972 (2021)","journal-title":"IEEE Internet Things J."},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jpdc.2022.12.009","volume":"175","author":"H Asgharzadeh","year":"2023","unstructured":"Asgharzadeh, H., Ghaffari, A., Masdari, M., Gharehchopogh, F.S.: Anomaly-based intrusion detection system in the Internet of Things using a convolutional neural network and multi-objective enhanced Capuchin Search Algorithm. J. Parallel Distrib. Comput. 175, 1\u201321 (2023)","journal-title":"J. Parallel Distrib. Comput."},{"key":"2_CR20","doi-asserted-by":"crossref","unstructured":"Abd Elaziz, M., Al-qaness, M.A., Dahou, A., Ibrahim, R.A., Abd El-Latif, A.A.: Intrusion detection approach for cloud and IoT environments using deep learning and Capuchin Search Algorithm. Adv. Eng. Softw. 176, Article no. 103402 (2023)","DOI":"10.1016\/j.advengsoft.2022.103402"},{"issue":"13","key":"2_CR21","doi-asserted-by":"publisher","first-page":"11888","DOI":"10.1109\/JIOT.2023.3244810","volume":"10","author":"A Thakkar","year":"2023","unstructured":"Thakkar, A., Lohiya, R.: Attack classification of imbalanced intrusion data for IoT network using ensemble learning-based deep neural network. IEEE Internet Things J. 10(13), 11888\u201311895 (2023)","journal-title":"IEEE Internet Things J."},{"key":"2_CR22","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, Y., Javaheri, Z., Almutairi, L., Moghadamnejad, N., Younes, O.S.: Federated deep learning for anomaly detection in the Internet of Things. Comput. Electr. Eng. 108, Article no. 108651 (2023)","DOI":"10.1016\/j.compeleceng.2023.108651"},{"issue":"9","key":"2_CR23","doi-asserted-by":"publisher","first-page":"6435","DOI":"10.1109\/TII.2021.3130248","volume":"18","author":"S Latif","year":"2021","unstructured":"Latif, S., Huma, Z.E., Jamal, S.S., et al.: Intrusion detection framework for the Internet of Things using a dense random neural network. IEEE Trans. Ind. Inf. 18(9), 6435\u20136444 (2021)","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"2","key":"2_CR24","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1111\/coin.12473","volume":"38","author":"HN Bhor","year":"2022","unstructured":"Bhor, H.N., Kalla, M.: TRUST-based features for detecting the intruders in the Internet of Things network using deep learning. Comput. Intell. 38(2), 438\u2013462 (2022)","journal-title":"Comput. Intell."},{"issue":"5","key":"2_CR25","doi-asserted-by":"publisher","first-page":"2894","DOI":"10.1109\/TNSE.2022.3184975","volume":"10","author":"Y Zhang","year":"2022","unstructured":"Zhang, Y., Yang, C., Huang, K., Li, Y.: Intrusion detection of industrial internet-of-things based on reconstructed graph neural networks. IEEE Trans. Netw. Sci. Eng. 10(5), 2894\u20132905 (2022)","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"2_CR26","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.ins.2022.03.065","volume":"598","author":"A Basati","year":"2022","unstructured":"Basati, A., Faghih, M.M.: Efficient network intrusion detection in IoT using parallel deep auto-encoders. Inf. Sci. 598, 57\u201374 (2022)","journal-title":"Inf. Sci."},{"key":"2_CR27","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.comcom.2022.12.010","volume":"199","author":"SM Kasongo","year":"2023","unstructured":"Kasongo, S.M.: A deep learning technique for intrusion detection system using a Recurrent Neural Networks based framework. Comput. Commun. 199, 113\u2013125 (2023)","journal-title":"Comput. Commun."},{"key":"2_CR28","doi-asserted-by":"crossref","unstructured":"Sanju, P.: Enhancing intrusion detection in IoT systems: a hybrid metaheuristics-deep learning approach with ensemble of recurrent neural networks. J. Eng. Res. Article no. 100122 (2023)","DOI":"10.1016\/j.jer.2023.100122"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Sharma, B., Sharma, L., Lal, C., Roy, S.: Anomaly based network intrusion detection for IoT attacks using deep learning technique. Comput. Electr. Eng. 107, Article no. 108626 (2023)","DOI":"10.1016\/j.compeleceng.2023.108626"},{"key":"2_CR30","doi-asserted-by":"publisher","unstructured":"Rouzbahani, H.M., Bahrami, A.H., Karimipour, H.: A snapshot ensemble deep neural network model for attack detection in industrial Internet of Things. In: Karimipour, H., Derakhshan, F. (eds.) AI-Enabled Threat Detection and Security Analysis for Industrial IoT, pp. 181\u2013194. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-76613-9_10","DOI":"10.1007\/978-3-030-76613-9_10"},{"key":"2_CR31","doi-asserted-by":"publisher","first-page":"73907","DOI":"10.1109\/ACCESS.2020.2988055","volume":"8","author":"J Pacheco","year":"2020","unstructured":"Pacheco, J., Benitez, V.H., Felix-Herran, L.C., Satam, P.: Artificial neural networks-based intrusion detection system for Internet of Things fog nodes. IEEE Access 8, 73907\u201373918 (2020)","journal-title":"IEEE Access"},{"issue":"5","key":"2_CR32","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.dcan.2021.03.005","volume":"7","author":"W Fang","year":"2021","unstructured":"Fang, W., Zhang, W., Yang, W., Li, Z., Gao, W., Yang, Y.: Trust management-based and energy-efficient hierarchical routing protocol in wireless sensor networks. Digit. Commun. Netw. 7(5), 470\u2013478 (2021)","journal-title":"Digit. Commun. Netw."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-5588-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,12]],"date-time":"2024-08-12T18:03:11Z","timestamp":1723485791000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-5588-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819755875","9789819755882"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-5588-2_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"13 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 August 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 August 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/2024\/index.htm","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}