Comparative analysis of dimensionality reduction techniques for cybersecurity in the SWaT dataset
Özet
The Internet of Things (IoT) has revolutionized the functionality and efciency of
distributed cyber-physical systems, such as city-wide water treatment systems. However, the increased connectivity also exposes these systems to cybersecurity threats.
This research presents a novel approach for securing the Secure Water Treatment
(SWaT) dataset using a 1D Convolutional Neural Network (CNN) model enhanced
with a Gated Recurrent Unit (GRU). The proposed method outperforms existing
methods by achieving 99.68% accuracy and an F1 score of 98.69%. Additionally, the
paper explores dimensionality reduction methods, including Autoencoders, Generalized Eigenvalue Decomposition (GED), and Principal Component Analysis (PCA).
The research fndings highlight the importance of balancing dimensionality reduction with the need for accurate intrusion detection. It is found that PCA provided
better performance compared to the other techniques, as reducing the input dimension by 90.2% resulted in only a 2.8% and 2.6% decrease in the accuracy and F1
score, respectively. This study contributes to the feld by addressing the critical need
for robust cybersecurity measures in IoT-enabled water treatment systems, while
also considering the practical trade-of between dimensionality reduction and intrusion detection accuracy.
