Investigation on identify the multiple issues in IoT devices using Convolutional Neural Network

被引：0

作者：

Thouti S. ^{[1
]}

Venu N. ^{[2
]}

Rinku D.R. ^{[1
]}

Arora A. ^{[1
]}

Rajeswaran N. ^{[3
]}

机构：

[1] Department of Electronics and Communication Engineering, CVR College of Engineering, Hyderabad

[2] Department of Electronics and Communication Engineering, Balaji Institute of Technology and Science, Warangal

[3] Department of Electrical and Electronics Engineering, Malla Reddy Institute of Engineering and Technology, Hyderabad

来源：

Measurement: Sensors | 2022年 / 24卷

关键词：

Battery; CNN; Connectivity and security; IoT; Power supply;

D O I：

10.1016/j.measen.2022.100509

中图分类号：

学科分类号：

摘要：

The Internet of Things (IoT) is an innovative technology that makes it possible for physical objects like sensors, cameras, household appliances, and other objects to interact and communicate with one another. The Internet of Things (IoT) devices may exchange critical data, which makes battery/power, connectivity, and security issues crucial. An automated system for identifying and reporting abnormalities to a central controller is a prerequisite for this. In order to distinguish between approved and legitimate IoT devices, this method should be able to. IoT devices that are malicious, non-IoT devices that are malicious, and other man-in-the-middle traffic sources must all be isolated to prevent noncompliance. For improved QoS management, this aids in the formulation of administrative rules and the regulation and enforcement of network traffic. A framework-based Convolutional Neural Network (CNN) is suggested in this research to discover the aforementioned problems in IoT devices. A system that classifies IoT devices into their respective categories and reliably identifies new entries has been developed based on CNN. The findings demonstrate that CNN is capable of classifying IoT devices into the appropriate categories with the necessary accuracy as well as identifying between IoT and non-IoT devices with better accuracy. © 2022 The Authors

引用

共 20 条

[11]

Razzaque M.A., Milojevic-Jevric M., Palade A., Clarke S., Middleware for internet of things: a survey, IEEE Internet of Things Journal, 3, pp. 70-95, (2016)

[12]

Ahn N.Y., Lee D.H., Security of IoT device: perspective forensic/anti-forensic issues on invalid area of NAND flash memory, IEEE Access, 10, pp. 74207-74219, (2022)

[13]

Cai Z., Bourgeois A., Tong W., Guest editorial: special issue on internet of things, Tsinghua Science and Technology, 22, pp. 343-344, (2017)

[14]

Sciancalepore S., Pietro R.D., PPRQ: privacy-preserving MAX/MIN range queries in IoT networks, IEEE Internet of Things Journal, 8, pp. 5075-5092, (2021)

[15]

Ullah, Mahmoud Q.H., Design and development of RNN anomaly detection model for IoT networks, IEEE Access, 10, pp. 62722-62750, (2022)

[16]

Liang F., Yu W., Liu X., Griffith D., Golmie N., Toward edge-based deep learning in industrial internet of things, IEEE Internet of Things Journal, 7, pp. 4329-4341, (2020)

[17]

Chen B., Xiang T., Ma M., He D., Liao X., CL-ME: efficient certificateless matchmaking encryption for internet of things, IEEE Internet of Things Journal, 8, pp. 15010-15023, (2021)

[18]

Nandy T., Et al., Review on security of internet of things authentication mechanism, IEEE Access, 7, pp. 151054-151089, (2019)

[19]

Kamilaris, Pitsillides A., Mobile phone computing and the internet of things: a survey, IEEE Internet of Things Journal, 3, pp. 885-898, (2016)

[20]

Wang M.D., Dohler M., Mao X., Hu R.Q., Wang H., Guest editorial - special issue on internet of things (IoT): architecture, protocols and services, IEEE Sensors Journal, 13, pp. 3505-3510, (2013)

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