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Development of Unlicensed Identification Technique in the IOT environment and computer application

JOURNAL:MAZEDAN INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS

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# Authors First Online DOI Downloads Citations
1. Narendra Ghorsad, Sadik Khan, Yogesh K Sharma, Dyuti Banerjee 27 Jun 2022 NA 7 0
Abstract

Since the usage of the Internet of Things has been increasingly ubiquitous in human life in terms of industrial growth over the past several decades, the number of cybercriminals has also increased, and various methods of carrying out cyber-attacks are currently being researched. In this context, a number of researchers have developed a number of IoT intrusion detection systems to prevent attacks on IoT and to provide robust security to the IoT system. These IoT intrusion detection systems are based on criteria such as system search techniques, authentication strategy, and deployment. This paper provides a detailed review of modern IoT intrusion detection systems (IDS) as well as the methods, deployment tactics, authentication schemes, and datasets that are often utilised while developing IDS. We have covered how the system that was suggested before can identify attacks, how the Internet of Things (IoT) may give improved security, as well as how IoT assaults that have already happened have been categorised. It is now being discussed how to make the Internet of Things more secure and how to stop similar attacks from happening in the future.

Keywords

Internet of Things, Intrusion Detection, Network attack, Machine Learning

References

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    [2]   A. K. Alharam and W. El-madany, "Complexity of Cyber Security Architecture for IoT Healthcare Industry: A Comparative Study," 2017 5th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), Prague, 2017, pp. 246-250. DOI: 10.1109/FiCloudW.2017.100

    [3]   T. Nathezhtha and V. Yaidehi, "Cloud Insider Attack Detection Using Machine Learning," 2018 International Conference on Recent Trends in Advanced Computing (ICRTAC), Chennai, India, 2018, pp. 60-65. DOI: 10.1109/ICRTAC.2018.8679338.

    [4]   M. Aldairi, L. Karimi and J. Joshi, "A Trust Aware Unsupervised Learning Approach for Insider Threat Detection," 2019 IEEE 20th International Conference on Information Reuse and Integration for Data Science (IRI), Los Angeles, CA, USA, 2019, pp. 89-98. DOI: 10.1109/IRI.2019.00027