Adversarial Neural Networks for Blind Smart Grid Attack 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 increasingly orchestrated by cyber adversaries using artificial intelligence, compromising grid operations and user equipment.
Innovation Solution
Training an adversarial neural network to simulate stealthy blind false data injection attacks using supervised learning, generating and verifying attack vectors with a substitute bad data detection threshold to create 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 designed by modern cyber attackers cannot be detected
Solution Approach 1:
The patent inverts the traditional approach by training an adversarial neural network to generate stealthy attacks, then using this adversarial model to detect and defend against such attacks. Instead of directly trying to detect attacks, the system first understands how to create them, then uses this knowledge for defense through adversarial verification and countermeasure generation.
Solution Approach 2:
The system performs preliminary training of the adversarial neural network to generate attack vectors before actual attacks occur. This pre-trained model serves as a detection tool that can identify stealthy attacks in real-time, allowing the system to prepare countermeasures in advance rather than reacting after attacks have succeeded.
2Reliability
If adversarial neural networks are trained to simulate stealthy attacks, then detection capability improves, but system complexity increases
Solution Approach 1:
The patent creates a simplified copy or substitute model of the bad data detection mechanism (substitute BDD) that runs alongside the main detection system. This substitute model is trained to recognize attack patterns without requiring full complexity of the original detection algorithm, reducing computational burden while maintaining detection effectiveness.
Solution Approach 2:
The detection system is segmented into multiple components: the adversarial neural network for attack simulation, the substitute BDD for verification, and the countermeasure generation module. Each component handles a specific aspect of attack detection and defense, distributing computational complexity across separate modules rather than concentrating it in a single complex system.
3Object-affected harmful factors
If substitute bad data detection threshold is used for filtering attack vectors, then false data injection impact is reduced, but detection accuracy may be compromised
Solution Approach 1:
The system uses feedback loops where the adversarial neural network generates attack vectors, the substitute BDD verifies them against the threshold, and the results feed back into training and countermeasure generation. This continuous feedback allows the system to refine its detection threshold and improve accuracy over time while maintaining protection against false data injections.
Solution Approach 2:
The substitute bad data detection threshold is dynamically adjusted based on training data and attack patterns. Rather than using a fixed threshold, the system adapts the parameter to balance detection accuracy and false positive rates, optimizing performance for different attack scenarios while reducing the impact of false data injections.
Data Source
Figure 1
Figure 2
Figure 3
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.