IoT Security Implementation using Machine Learning

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Muhammad Zunnurain Hussain
Muhammad Zulkifl Hasan
Summaira Nosheen
Ali Moiz Qureshi
Adeel Ahmad Siddiqui
Muhammad Atif Yaqub
Saad Hussain Chuhan
Afshan Belal
Muzammil Mustafa


Internet of Things, IoT Security, Machine Learning, Supervised Learning, Unsupervised Learning, Deep Learning, Cyber-attacks, Threat Detection, Threat Mitigation, Security Measures.


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.