{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T00:49:35Z","timestamp":1777682975276,"version":"3.51.4"},"reference-count":18,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JHS"],"published-print":{"date-parts":[[2024,5,10]]},"abstract":"<jats:p>IoT networks can be defined as groups of physically connected things and devices that can connect to the Internet and exchange data with one another. Since enabling an increasing number of internets of things devices to connect with their networks, organizations have become more vulnerable to safety issues and attacks. A major drawback of previous research is that it can find out prior seen types only, also any new device types are considered anomalous. In this manuscript, IoT device type detection utilizing Training deep quantum neural networks optimized with a Chimp optimization algorithm for enhancing IOT security (IOT-DTI-TDQNN-COA-ES) is proposed. The proposed method entails three phases namely data collection, feature extraction and detection. For Data collection phase, real network traffic dataset from different IoT device types are collected. For feature mining phase, the internet traffic features are extracted through automated building extraction (ABE) method. IoT device type identification phase, Training deep quantum neural networks (TDQNN) optimized with Chimp optimization algorithm (COA) is utilized to detect the category of IoT devices as known and unknown device. IoT network is implemented in Python. Then the simulation performance of the proposed IOT-DTI-TDQNN-COA-ES method attains higher accuracy as26.82% and 23.48% respectively, when compared with the existing methods.<\/jats:p>","DOI":"10.3233\/jhs-230028","type":"journal-article","created":{"date-parts":[[2023,11,3]],"date-time":"2023-11-03T12:12:53Z","timestamp":1699013573000},"page":"191-201","source":"Crossref","is-referenced-by-count":4,"title":["IoT device type identification using training deep quantum neural networks optimized with a chimp optimization algorithm for enhancing IoT security"],"prefix":"10.1177","volume":"30","author":[{"given":"C.P.","family":"Shirley","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India"}]},{"given":"Jaydip","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sanskriti University, Mathura (UP), India"}]},{"given":"Kantilal","family":"Pitambar\u00a0Rane","sequence":"additional","affiliation":[{"name":"Department of Electronics and Telecommunications Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, Maharashtra, India"}]},{"given":"Narendra","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, RNS institute of technology, Bengaluru, India"}]},{"given":"Deevi","family":"Radha\u00a0Rani","sequence":"additional","affiliation":[{"name":"Department of Advanced CSE, VFSTR Deemed to be University, Guntur, India"}]},{"given":"Kuntamukkula","family":"Harshitha","sequence":"additional","affiliation":[{"name":"School of Architecture, Koneru Lakshmaiah Educational foundation, Vaddeswaram, Guntur district, India"}]},{"given":"Mohit","family":"Tiwari","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bharati Vidyapeeth\u2019s College of Engineering, Paschim Vihar, Delhi, India"}]}],"member":"179","reference":[{"key":"10.3233\/JHS-230028_ref1","doi-asserted-by":"crossref","unstructured":"S.W.\u00a0Azumah, N.\u00a0Elsayed, V.\u00a0Adewopo, Z.S.\u00a0Zaghloul and C.\u00a0Li, A deep lstm based approach for intrusion detection iot devices network in smart home, in: 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), IEEE, 2021 pp.\u00a0836\u2013841.","DOI":"10.1109\/WF-IoT51360.2021.9596033"},{"key":"10.3233\/JHS-230028_ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2019.100112"},{"key":"10.3233\/JHS-230028_ref3","doi-asserted-by":"crossref","unstructured":"J.\u00a0Bao, B.\u00a0Hamdaoui and W.K.\u00a0Wong, Iot device type identification using hybrid deep learning approach for increased IoT security, in: 2020 International Wireless Communications and Mobile Computing (IWCMC), IEEE, 2020 June, pp.\u00a0565\u2013570.","DOI":"10.1109\/IWCMC48107.2020.9148110"},{"issue":"1","key":"10.3233\/JHS-230028_ref4","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1038\/s41467-020-14454-2","article-title":"Training deep quantum neural networks","volume":"11","author":"Beer","year":"2020","journal-title":"Nature communications"},{"issue":"4","key":"10.3233\/JHS-230028_ref5","doi-asserted-by":"publisher","first-page":"2906","DOI":"10.1109\/JIOT.2021.3095255","article-title":"DDI: A novel architecture for joint active user detection and IoT device identification in grantfree NOMA systems for 6G and beyond networks","volume":"9","author":"Dev","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"key":"10.3233\/JHS-230028_ref6","doi-asserted-by":"publisher","first-page":"5835","DOI":"10.1007\/s12652-020-02126-4","article-title":"IoT using machine learning security enhancement in video steganography allocation for Raspberry Pi","volume":"12","author":"Karthika","year":"2021","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"10.3233\/JHS-230028_ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113338"},{"key":"10.3233\/JHS-230028_ref8","unstructured":"K.R.\u00a0Kumar, C.\u00a0Hemanth, C.A.\u00a0Kumar, K.M.\u00a0Sahith and G.A.\u00a0Prasanth, IoT device identification through network traffic analysis, Int. Res. J. Modern. Eng. Technol. Sci 2(06) (2020)."},{"issue":"22","key":"10.3233\/JHS-230028_ref9","doi-asserted-by":"publisher","first-page":"22133","DOI":"10.1109\/JIOT.2021.3106898","article-title":"Deep learning in security of Internet of things","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Internet of Things Journal"},{"key":"10.3233\/JHS-230028_ref10","doi-asserted-by":"crossref","unstructured":"Y.\u00a0Luo, X.\u00a0Chen, N.\u00a0Ge, W.\u00a0Feng and J.\u00a0Lu, Transformer-based device type identification in heterogeneous IoT traffic, IEEE Internet of Things Journal (2022).","DOI":"10.1109\/JIOT.2022.3221967"},{"key":"10.3233\/JHS-230028_ref12","doi-asserted-by":"publisher","first-page":"200219","DOI":"10.1109\/ACCESS.2020.3032469","article-title":"Device type identification via network traffic and lightweight convolutional neural network for Internet of things","volume":"8","author":"Qing","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JHS-230028_ref13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.107810"},{"key":"10.3233\/JHS-230028_ref14","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1016\/j.ins.2023.01.148","article-title":"EHDHE: Enhancing security of healthcare documents in IOT-enabled digital healthcare ecosystems using blockchain","volume":"629","author":"Sharma","year":"2023","journal-title":"Information Sciences"},{"key":"10.3233\/JHS-230028_ref15","first-page":"1","article-title":"Long text feature extraction network with data augmentation","author":"Tang","year":"2022","journal-title":"Applied Intelligence"},{"key":"10.3233\/JHS-230028_ref16","doi-asserted-by":"crossref","unstructured":"F.\u00a0Yin, L.\u00a0Yang, Y.\u00a0Wang and J.\u00a0Dai, Iot etei: End-to-end iot device identification method, in: 2021 IEEE on Dependable and Secure Computing (DSC), IEEE, 2021, pp.\u00a01\u20138.","DOI":"10.1109\/DSC49826.2021.9346251"},{"issue":"8","key":"10.3233\/JHS-230028_ref17","doi-asserted-by":"publisher","first-page":"2660","DOI":"10.3390\/s21082660","article-title":"Automated iot device identification based on full packet information using real-time network traffic","volume":"21","author":"Yousefnezhad","year":"2021","journal-title":"Sensors"},{"key":"10.3233\/JHS-230028_ref18","doi-asserted-by":"publisher","first-page":"7981","DOI":"10.1109\/ACCESS.2020.2964646","article-title":"Large-scale IoT devices firmware identification based on weak password","volume":"8","author":"Yu","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JHS-230028_ref19","first-page":"1","article-title":"Deep learning-based security behaviour analysis in IoT environments: A survey","volume":"2021","author":"Yue","year":"2021","journal-title":"Security and Communication Networks"}],"container-title":["Journal of High Speed Networks"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JHS-230028","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T08:44:37Z","timestamp":1777452277000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JHS-230028"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,10]]},"references-count":18,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jhs-230028","relation":{},"ISSN":["1875-8940","0926-6801"],"issn-type":[{"value":"1875-8940","type":"electronic"},{"value":"0926-6801","type":"print"}],"subject":[],"published":{"date-parts":[[2024,5,10]]}}}