Federated Learning-Inspired Technique for Attack Classification in IoT Networks

More than 10-billion physical items are being linked to the internet to conduct activities more independently and with less human involvement owing to the Internet of Things (IoT) technology. IoT networks are considered a source of identifiable data for vicious attackers to carry out criminal action...

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Bibliographic Details
Published in:Mathematics (Basel) 2022-06, Vol.10 (12), p.2141
Main Authors: Ahanger, Tariq Ahamed, Aldaej, Abdulaziz, Atiquzzaman, Mohammed, Ullah, Imdad, Yousufudin, Muhammad
Format: Article
Language:English
Subjects:
Algorithms
Anomalies
Architecture
Artificial intelligence
Classification
Communication
Compatible hardware
Crime
DDoS attack
Decision making
Deep learning
Efficiency
False alarms
Federated learning
Identification
Internet of Things
Machine learning
Neural networks
security
Sensors
Servers
Statistical analysis
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Summary:More than 10-billion physical items are being linked to the internet to conduct activities more independently and with less human involvement owing to the Internet of Things (IoT) technology. IoT networks are considered a source of identifiable data for vicious attackers to carry out criminal actions using automated processes. Machine learning (ML)-assisted methods for IoT security have gained much attention in recent years. However, the ML-training procedure incorporates large data which is transferable to the central server since data are created continually by IoT devices at the edge. In other words, conventional ML relies on a single server to store all of its data, which makes it a less desirable option for domains concerned about user privacy. The Federated Learning (FL)-based anomaly detection technique, which utilizes decentralized on-device data to identify IoT network intrusions, represents the proposed solution to the aforementioned problem. By exchanging updated weights with the centralized FL-server, the data are kept on local IoT devices while federating training cycles over GRUs (Gated Recurrent Units) models. The ensemble module of the technique assesses updates from several sources for improving the accuracy of the global ML technique. Experiments have shown that the proposed method surpasses the state-of-the-art techniques in protecting user data by registering enhanced performance measures of Statistical Analysis, Energy Efficiency, Memory Utilization, Attack Classification, and Client Accuracy Analysis for the identification of attacks.
ISSN:2227-7390
2227-7390
DOI:10.3390/math10122141