EL-NAHL: Exploring labels autoencoding in augmented hidden layers of feedforward neural networks for cybersecurity in smart grids

Reliability and security of power distribution and data traffic in smart grid (SG) are very important for industrial control systems (ICS). Indeed, SG cyber-physical connectivity is subject to several vulnerabilities that can damage or disrupt its process immunity via cyberthreats. Today's ICSs are experiencing highly complex data change and dynamism, increasing the complexity of detecting and mitigating cyberattacks. Subsequently, and since Machine Learning (ML) is widely studied in cybersecurity, the objectives of this paper are twofold. First, for algorithmic simplicity, a small-scale ML algorithm that attempts to reduce computational costs is proposed. The algorithm adopts a neural network with an augmented hidden layer (NAHL) to easily and efficiently accomplish the learning procedures. Second, to solve the data complexity problem regarding rapid change and dynamism, a label autoencoding approach is introduced for Embedding Labels in the NAHL (EL-NAHL) architecture to take advantage of labels propagation when separating data scatters. Furthermore, to provide a more realistic analysis by addressing real-world threat scenarios, a dataset of an electric traction substation used in the high-speed rail industry is adopted in this work. Compared to some existing algorithms and other previous works, the achieved results show that the proposed EL-NAHL architecture is effective even under massive dynamically changed and imbalanced data.