Improving the IoT Attack Classification Mechanism with Data Augmentation for Generative Adversarial Networks

The development of IoT technology has made various IoT applications and services widely used. Because IoT devices have weak information security protection capabilities, they are easy targets for cyber attacks. Therefore, this study proposes MLP-based IoT attack classification with data augmentation...

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Bibliographic Details
Published in:Applied sciences 2023-12, Vol.13 (23), p.12592
Main Authors: Chu, Hung-Chi, Lin, Yu-Jhe
Format: Article
Language:English
Subjects:
Accuracy
Classification
Cybersecurity
Cyberterrorism
data augmentation
Data transmission
Datasets
Deep learning
discriminator
generative adversarial networks
generator
Internet of Things
Intrusion detection systems
Malware
Neural networks
Software upgrading
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Summary:The development of IoT technology has made various IoT applications and services widely used. Because IoT devices have weak information security protection capabilities, they are easy targets for cyber attacks. Therefore, this study proposes MLP-based IoT attack classification with data augmentation for GANs. In situations where the overall classification performance is satisfactory but the performance of a specific class is poor, GANs are employed as a data augmentation mechanism for that class to enhance its classification performance. The experimental results indicate that regardless of whether the training dataset is BoT-IoT or TON-IOT, the proposed method significantly improves the classification performance of classes with insufficient training data when using the data augmentation mechanism with GANs. Furthermore, the classification accuracy, precision, recall, and F1-score performance all exceed 90%.
ISSN:2076-3417
2076-3417
DOI:10.3390/app132312592