Internet of Things: A Comprehensive Study on Machine Learning-based Intrusion Detection approaches

Authors

Priyanka Gupta
Department of computer science and engineering Maulana Azad National Institute of Technology Bhopal, India
Lokesh Yadav
Department of computer science and engineering Maulana Azad National Institute of Technology Bhopal, India
Deepak Singh Tomar
Department of computer science and engineering Maulana Azad National Institute of Technology Bhopal, India

Synopsis

The Internet of Things (IoT) connects billions of interconnected devices that can exchange information with each other with minimal user intervention. The goal of IoT to become accessible to anyone, anytime, and anywhere. IoT has engaged in multiple fields, including education, healthcare, businesses, and smart home. Security and privacy issues have been significant obstacles to the widespread adoption of IoT. IoT devices cannot be entirely secure from threats; detecting attacks in real-time is essential for securing devices. In the real-time communication domain and especially in IoT, security and protection are the major issues. The resource-constrained nature of IoT devices makes traditional security techniques difficult. In this paper, the research work carried out in IoT Intrusion Detection System is presented. The Machine learning methods are explored to provide an effective security solution for IoT Intrusion Detection systems. Then discussed the advantages and disadvantages of the selected methodology. Further, the datasets used in IoT security are also discussed. Finally, the examination of the open issues and directions for future trends are also provided.

WREC21
Published
September 22, 2021
Online ISSN
2582-3922