Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids

2026-06-18 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Energy Storage & Grid Technology · Depth: Expert, quick

Summary

Pseudo-Feature Padding, a novel defense framework, strengthens Deep Neural Networks (DNNs) against False Data Injection Attacks (FDIA) in Cyber-Physical Systems (CPS), particularly power grids. The method introduces an additional input layer that pads input samples with pseudo-feature values derived from their statistical distribution. This randomized, data-aware padding increases input dimensionality, rendering adversarial attacks computationally infeasible due to non-transferable perturbations and unpredictable structure. The approach is lightweight, model-agnostic, and requires no core architecture modifications, making it highly deployable. Evaluated on critical power grid state estimation using IEEE 14-bus, 30-bus, 118-bus, and 300-bus systems, experiments demonstrate significant robustness improvement with negligible performance impact, effectively mitigating attacks that bypass conventional defenses.

Key takeaway

For AI Security Engineers tasked with protecting Cyber-Physical Systems from False Data Injection Attacks, you should evaluate Pseudo-Feature Padding. This model-agnostic defense strengthens Deep Neural Networks by randomizing input dimensionality, making adversarial attacks computationally infeasible without modifying core architectures. Integrating this lightweight approach can significantly improve system robustness and mitigate attacks that bypass conventional defenses, ensuring operational integrity in critical infrastructure like power grids.

Key insights

Pseudo-Feature Padding defends DNNs in CPS against FDIA by randomizing input dimensionality, making adversarial attacks computationally infeasible.

Principles

• Randomized input padding enhances DNN robustness.
• Data-aware pseudo-features increase attack complexity.
• Model-agnostic defenses are highly deployable.

Method

An additional input layer performs padding using pseudo-feature values derived from input statistical distribution, increasing input dimensionality in a randomized and data-aware manner.

In practice

• Apply to power grid state estimation.
• Integrate into existing DNN architectures.
• Mitigate FDIA in Cyber-Physical Systems.

Topics

• False Data Injection Attacks
• Cyber-Physical Systems
• Deep Neural Networks
• Pseudo-Feature Padding
• Power Grid Security
• Adversarial Robustness

Best for: AI Scientist, AI Security Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.