Using Siamese Networks and Autoencoders for Feature Reduction in IoT Security
Chonghao Pei, Lotfi Mhamdi · 2024
In the development of the Internet of Things (IoT), machine learning has become a significant solution to cyberattack challenges. However, the vast and heterogeneous IoT network traffic poses challenges for resource-constrained devices. To address this concern, this paper introduces the SiaAE approach, integrating the Siamese network with autoencoders, aimed at reducing the feature dimensionality of IoT network traffic data. This design enables the autoencoder to differentiate between detailed information among samples of the same or different categories while reducing dimensionality. We evaluated SiaAE on three public datasets: CICIDS2017, N-BaIoT, and TON-IoT. The results demonstrate that SiaAE outperforms traditional Principal Component Analysis (PCA) and standard autoencoders (AE) in feature reduction and class separation, thereby enhancing the accuracy of classification tasks. Specifically, SiaAE achieved accuracy rates of 99.94%, 99.97%, and 97.99% on the three datasets, respectively. Its effectiveness is proven in improving the performance of machine learning models for network security.