2025/10/20 by Mohammadamin Lari, Lari, Mohammadamin
Computer Science · Engineering · #Adversarial Robustness in Machine Learning #Anomaly Detection Techniques and Applications #FOS: Electrical engineering #Smart Grid Security and Resilience #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2510.17155
openalex publication_date 2025/10/20 · openalex created_date 2025/10/22 · openalex updated_date 2026/07/28
This paper introduces a novel two-stage framework for online mitigation of False Data Injection (FDI) signals to improve the resiliency of Networked Control Systems (NCSs) and ensure their safe operation in the presence of malicious activities. The first stage involves meta learning to select a base time series forecasting model within a stacked ensemble learning architecture. This is achieved by converting time series data into scalograms using continuous wavelet transform, which are then split into image frames to generate a scalo-temporal representation of the data and to distinguish between different complexity levels of time series data based on an entropy metric using a convolutional neural network. In the second stage, the selected model mitigates false data injection signals in real-time. The proposed framework's effectiveness is demonstrated through rigorous simulations involving the formation control of differential drive mobile robots. By addressing the security challenges in NCSs, this framework offers a promising approach to maintaining system integrity and ensuring operational safety.