Strengthening the Security of IoT Devices Through Federated Learning: A Comprehensive Study

Title

Strengthening the Security of IoT Devices Through Federated Learning: A Comprehensive Study

Creator

Raj A.; Sharma V.; Rani S.; Shanu A.K.; Kumar N.

Description

There is a strong need for having an operative security framework which can help in making IoT (Internet of Things) devices more secure and reliable which can further protect from adversarial intrusions. Federated Learning, due to its decentralized architecture, has emerged as one of the ideal choices by the research practitioners in order to protect sensitive data from wide IoT-based attacks like DoS (Denial of Service) attack, Device Tampering, Sensor-Data manipulation etc. This paper discusses the significance of federated learning in addressing security concerns with IoT (Internet of Things) devices and how those issues can be minimized with the use of Federated Learning has been deliberated with the help of comparative analysis. In order to perform this comparative analysis, we investigated the published work in FL based IoT application for the last five years i.e., 2018-2022. We have defined a few inclusion/exclusion criteria and based on that we selected the desired paper and provided a comprehensive solution to IoT based applications using FL approach. Federated learning offers an optimistic approach to intensify security in IoT environments by enabling collaborative model training while preserving information privacy. In this paper a framework named Federated AI Technology Enabler (FATE) has been envisaged which is one of the recommended frameworks in safeguarding security and privacy measures of IoT devices. 2024 IEEE.

Source

2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions), ICRITO 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Adversarial Intrusions; Device Tampering; Federated AI Technology Enabler (FATE); Federated Learning; Inclusion/Exclusion Criteria; Internet of Things (IoT); Sensor-Data Manipulation

Coverage

Raj A., Scse Galgotias University, Greater Noida, India; Sharma V., Christ (Deemed to Be University), Department of Computational Sciences, Delhi-NCR, India; Rani S., Amity Institute of Information Technology, Amity University, Auup, Noida, India; Shanu A.K., Scse Galgotias University, Greater Noida, India; Kumar N., School of Computing, Dublin City University, Ireland

Rights

Restricted Access

Relation

ISBN: 979-835035035-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICRITO61523.2024.10522388

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85193834416&doi=10.1109%2fICRITO61523.2024.10522388&partnerID=40&md5=5374736862edd3a6aa1cb7497b681793