Adversarial Neural Networks for Blind False Data Injection Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing solutions fail to detect and mitigate stealthy blind false data injection attacks in smart grids, which are orchestrated by cyber adversaries leveraging artificial intelligence, compromising grid operations and user equipment.
Innovation Solution
Training an adversarial neural network using supervised learning to simulate stealthy blind false data injection attacks, employing an adversarial attack generation model and verification model to generate and filter attack vectors, enabling the creation of countermeasures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bad data detection mechanisms are used, then abnormally noisy measurements can be filtered, but stealthy false data injection attacks without topology knowledge cannot be detected
Solution Approach 1:
The patent replaces traditional mechanical bad data detection mechanisms with an artificial intelligence-based detection system. The AI model analyzes measurement data patterns and identifies stealthy false data injection attacks that traditional statistical methods cannot detect, thereby substituting conventional detection mechanics with intelligent adaptive detection.
Solution Approach 2:
The patent changes the detection parameters from simple statistical thresholds to complex AI-learned patterns and features. By transforming the detection criteria from fixed mechanical thresholds to dynamic AI-derived parameters, the system gains the ability to detect sophisticated attacks while maintaining robustness against noisy measurements.
2Reliability
If adversarial neural networks are trained to simulate stealthy blind false data injection attacks, then the ability to detect and counteract sophisticated attacks is enhanced, but the complexity of the security system increases
Solution Approach 1:
The patent applies preliminary action by training adversarial neural networks in advance to simulate various stealthy attack scenarios. This pre-training phase allows the system to learn attack patterns and develop detection strategies before actual attacks occur, enhancing security reliability without requiring complex real-time response mechanisms during attacks.
Solution Approach 2:
The patent uses copying by creating virtual replicas of attack vectors through adversarial neural networks. These simulated attack copies are used to train detection models and develop countermeasures, allowing the system to practice and improve security defenses against sophisticated attacks without exposing the actual grid to real threats.
Data Source
AI summary
Computer-implemented methods and systems for training an adversarial neural network to simulate a stealthy blind false data injection attack on a cyber physical system are provided. Supervised learning is used to generate an initial attack vector, by an adversarial attack generation model, based on inferred grid topology and historical measurements. A final attack vector is generated, by an adversarial verification model, based on a filtered subset of the initial attack vector utilizing a substitute bad data detection threshold, wherein the final attack vector enables creation of a counter measure.