Towards Development of Machine Learning Framework for Enhancing Security in Internet of Things. (17th May 2022)

• Record Type: Journal Article
• Authors: Paricherla, Mutyalaiah; Babu, Sallagundla; Phasinam, Khongdet; Pallathadka, Harikumar; Zamani, Abu Sarwar; Narayan, Vipul; Shukla, Surendra Kumar; Mohammed, Hussien Sobahi
• Other Names: Soni Mukesh Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-05-17
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/4477507
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : An IoT system is a smart network that connects all items to the Internet and exchanges data using Internet Engineering Task Force established protocols. As a consequence, everything is instantly accessible from any place and at any time. The Internet of Things (IoT) network is built on the backbone of tiny sensors embedded in common objects. There is no need for human intervention in the interactions of IoT devices. The Internet of Things (IoT) security risk cannot be ignored. Untrusted networks, such as the Internet, are utilized to provide remote access to IoT devices. As a result, IoT systems are susceptible to a broad range of harmful activities, including cyberattacks. If security problems are not addressed, critical information may be hacked at any time. This article describes a feature selection and machine learning-based paradigm for improving security in the Internet of Things. Because network data are inherently abundant, it must be reduced in size before processing. Dimension reduction is the process of constructing a subset of an original data collection that removes superfluous content from the essential data set. Dimension reduction is a data mining approach. To minimize the number of dimensions in a dataset, linear discriminant analysis (LDA) is used. Following that, the data set with fewer dimensions is put into machine learning predictors as a training set. The effectiveness of machine learning approaches has been assessed using a range of criteria.