A novel RF-SMOTE model to enhance the definite apprehensions for IoT security attacks

Gowda, V. Dankan; Prasad, K.D.V.; Gite, Pratik; Premkumar, S.; Hussain, Naziya; Chinamuttevi, Veera Sivakumar

doi:10.47974/JDMSC-1766

Open Access Research Article

A novel RF-SMOTE model to enhance the definite apprehensions for IoT security attacks

^*V. Dankan GowdaCorresponding authordankan.v@bmsit.inDepartment of Electronics and Communication EngineeringBangalore, KarnatakaB. M. S Institute of Technology and ManagementIndiaView full profile → , K.D.V. Prasadkdv.prasad@sibmhyd.edu.inAffiliation 1HyderabadSymbiosis Institute of Business ManagementAffiliation 2Symbiosis International (Deemed University), PuneIndiaView full profile → , Pratik Gitepratikgite135@gmail.comDepartment of Computer EngineeringMumbai, MaharashtraSt. John College of Engineering and Management, Palghar (East)IndiaView full profile → , S. Premkumarpremambal@gmail.comDepartment of Computer Science and EngineeringGreater Noida, Uttar PradeshGalgotias UniversityIndiaView full profile → , Naziya Hussainnaziyahussain@gmail.comSchool of ComputersIndore, Madhya PradeshIPS AcademyIndiaView full profile → , Veera Sivakumar Chinamutteviveera.siva@ibrict.edu.omDepartment of EngineeringUniveristy of Technology and Applied Sciences-IBRI, Sultanate of Oman IBRIView full profile →

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pp. 861–873Vol. 26Issue 3April 2023DOI: 10.47974/JDMSC-1766 Crossmark XML

Published Online:: 01 Apr 2023
Article type:: Research Article
Language:: EN
Article no.:: JDMSC-1766
Pages:: 861–873

Abstract

The Internet of Things (IoT) environment must prioritise security because of the IoT’s significant attack susceptibility for a variety of reasons. The IoT attack detection technique or mitigation procedure is the extent of the currently available solutions. However, there are fewer autonomous security provider approaches available, and they are inappropriate for the IoT environment’s evolving threats. Because of this, there has to be a security system in place that can detect and counteract both known and unknown threats for the increasing number of IoT devices. Deep Learning (DL) based intrusion detection systems does not consider attack signatures and normal behavior to obtain detection rules, it requires large data sets for training and takes a longer time to train the data. Many a time, insufficient dataset configuration prompts the minimization of a learning calculation, bringing about over fitting and helpless grouping rates. The goal of the proposed study is to create and implement a machine learning-based self-protection system to safeguard the IoT environment.

Keywords

Attacks Security Random forest Machine learning IoT and Intrusion

Subject Classifications

Primary 93A30Secondary 49K1565Y10 Numerical algorithms for specific classes of architectures68M25 Computer security68M07 Mathematical problems of computer architecture68M18 Wireless sensor networks as related to computer science

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