IoT device type identification using training deep quantum neural networks optimized with a chimp optimization algorithm for enhancing IoT security

Article type: Research Article

Authors: Shirley, C.P.^{a; *} | Kumar, Jaydip^b | Pitambar Rane, Kantilal^c | Kumar, Narendra^d | Radha Rani, Deevi^e | Harshitha, Kuntamukkula^f | Tiwari, Mohit^g

Affiliations: [a] Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India | [b] Department of Computer Science and Engineering, Sanskriti University, Mathura (UP), India | [c] Department of Electronics and Telecommunications Engineering, Bharati Vidyapeeth College of Engineering, Navi Mumbai, Maharashtra, India | [d] Department of Electronics and Communication Engineering, RNS institute of technology, Bengaluru, India | [e] Department of Advanced CSE, VFSTR Deemed to be University, Guntur, India | [f] School of Architecture, Koneru Lakshmaiah Educational foundation, Vaddeswaram, Guntur district, India | [g] Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, Paschim Vihar, Delhi, India

Correspondence: [*] Corresponding author. E-mail: shirleycp@karunya.edu.

Abstract: 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.

Keywords: IOT security, device type identification, training deep quantum neural networks, chimp optimization algorithm

DOI: 10.3233/JHS-230028

Journal: Journal of High Speed Networks, vol. 30, no. 2, pp. 191-201, 2024