IoT Security Implementation using Machine Learning
Muhammad Zunnurain Hussain, Muhammad Zulkifl Hasan, Summaira Nosheen, Ali Moiz Qureshi, Adeel Ahmad Siddiqui, Muhammad Atif Yaqub, Saad Hussain Chuhan, Afshan Belal, Muzzamil Mustafa · Research Briefs on Information and Communication Technology Evolution · 2023
This paper focuses on the implementation of machine learning algorithms to improve security in the Internet of Things (IoT) environment. IoT is becoming an essential part of our daily lives, and security is a significant concern in this domain. Traditional security measures are not enough to protect IoT systems from the increasing number of cyber-attacks. Machine learning algorithms can provide a better and more effective approach to detecting and mitigating security threats in IoT systems. This paper discusses various machine learning techniques such as supervised learning, unsupervised learning, and deep learning, and how they can be applied to improve security in IoT systems. The paper also explores the challenges and opportunities of using machine learning in IoT security and provides recommendations for future research. Overall, this paper provides a comprehensive overview of the role of machine learning in IoT security implementation and highlights the need for further research in this area.