ISSN:2582-5208

www.irjmets.com

Paper Key : IRJ************344
Author: Avinash Ganne
Date Published: 11 Jan 2023
Abstract
The Internet of Things (IoT) has emerged as one of the most innovative and forward-thinking technologies, attracting interest from both the scientific community and the commercial community due to its financial allure. Artificial intelligencemachine learning (AIML) is required to deconstruct the data saved in the cloud framework in order to integrate various devices and associate gadgets with people. These IoT devices communicate with one another and exchange data utilizing the web and cloud-based network architecture by using their individual unique identities and the integrated sensor in each device. We are in a big data era where using AIML is essential to the cycle of swiftly and accurately analyzing the obtained cloud-based large data. IoT security issues include substantial challenges and risks, including hacking, data fraud, remote access, and cyberattacks. Despite the fact that AI is increasingly playing a larger role in the development of traditional cyber security, both cloud vulnerability and the networking of IoT devices pose serious risks. IoT devices that are not sufficiently protected run the danger of being used in DDoS (Distributed Denial of Service) attacks. These assaults reveal security flaws and interrupt services, which have a severe impact on customer satisfaction and economic output. Additionally, the great majority of IoT devices that are connected remotely and transported by a public entity are always in cyber danger. Because it is hard for humans to manage the network, AI and ML are advantageous and essential to our foreseeable networks. The above-mentioned issues in IoT security need the use of AIML as a security tool. The suggested approach to cloud network infrastructure security problems uses predictive analysis to foresee potential assaults. This research then develops the appropriate AIML application using 3GPP, MIMO, and datasets from CIADA and Packet. A more practical Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) are the results of the use of AI components, which have been crucial and utilized.
DOI LINK : 10.56726/IRJMETS32866 https://www.doi.org/10.56726/IRJMETS32866
Paper File to download :