Machine Learning-based Secure Cloud-IoT Monitoring System for Wireless Communications

P. Rajadurai

doi:https://doi.org/10.59232/AIR-V1I1P104

Research Article | Open Access | Download Full Text

Volume 1 | Issue 1 | Year 2023 | Article Id: AIR-V1I1P104 DOI: https://doi.org/10.59232/AIR-V1I1P104

Machine Learning-based Secure Cloud-IoT Monitoring System for Wireless Communications

P. Rajadurai

Received	Revised	Accepted	Published
14 Apr 2023	16 Jun 2023	28 Jun 2023	07 Jul 2023

Citation

P. Rajadurai. “Machine Learning-based Secure Cloud-IoT Monitoring System for Wireless Communications.” DS Journal of Artificial Intelligence and Robotics, vol. 1, no. 1, pp. 37-43, 2023.

Abstract

The Internet of Things (IoT) paradigm presents a number of security challenges because of the growing scale and mobility of the user base. Additionally, flexibility and connection with a cloud network are necessary for operating a central security architecture. In this paper, a novel secure IoT monitoring (Sec-IoTM) system has been proposed, which improves the security features in cloud-assisted IoT environments. The proposed system consists of three phases, namely spoof detection, trust monitoring, and authentication system. The spoof detection phase makes use of a support vector machine (SVM), which classifies the request as an attack or not. This will remove the unauthorized user at the initial stage. The trust monitoring phase contains an intrusion detection system and an intrusion prevention system that will detect and prevent the data from the attack. The authentication system will authenticate the user, and if it is an unauthenticated user, then it will be blocked. If the user is an authenticated one, then the message will be encrypted using Advanced Encryption Standard (AES) algorithm. The performance metrics of the proposed Sec-IoTM method have been evaluated in terms of parameters like performance metrics, detection times, and false positive rates. The proposed method achieves high accuracy, 96% better than existing techniques.

Keywords

Internet of Things (IoT), Machine learning, IoT monitoring system, Security, Encryption, AES algorithm

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