Attack and Anomaly Detection in IoT Devices using Federated Learning
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Department of Computer Science and Engineering(CSE), Islamic University of Technology(IUT), Board Bazar, Gazipur-1704, Bangladesh
Abstract
There has been a lot of focus from governments, universities, and businesses in recent
years on the intersection of cybersecurity and machine learning (ML) for the Internet of
Things (IoT). The Internet of Things (IoT) can be made more secure and efficient in the
future through the groundbreaking concept of federated cybersecurity (FC). This new
idea has the ability to efficiently identify security problems, implement countermeasures,
and contain them within the IoT network infrastructure. Cybersecurity goals are met
through the federation of a shared and learned model among several actors. Protecting
the insecure IoT environment requires privacy-aware ML models like federated learning
(FL).
Description
Supervised by
Dr. Md. Moniruzzaman,
Assistant Professor,
Co-Supervisor,
Mr. Imtiaj Ahmed Chowdhury,
Lecturer,
Department of Computer Science and Engineering(CSE),
Islamic University of Technology(IUT),
Board Bazar, Gazipur-1704, Bangladesh
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There has been a lot of focus from governments, universities, and businesses in recent years on the intersection of cybersecurity and machine learning (ML) for the Internet of Things (IoT). The Internet of Things (IoT) can be made more secure and efficient in the future through the groundbreaking concept of federated cybersecurity (FC). This new idea has the ability to efficiently identify security problems, implement countermeasures, and contain them within the IoT network infrastructure. Cybersecurity goals are met through the federation of a shared and learned model among several actors. Protecting the insecure IoT environment requires privacy-aware ML models like federated learning (FL).