Fault Detection Computer Device for False Data Injection Attacks
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Solution Overview
Problem
Smart grid systems are vulnerable to false data injection attacks, which can disrupt normal operations by introducing false sensor data, leading to unnecessary system actions or failure to detect actual faults, posing a significant cybersecurity risk to power grid substations.
Innovation Solution
A system comprising sensors and a fault detection computer device that monitors components, generates measurement data profiles, and determines the accuracy of fault indications to differentiate between real and spoofed data, enabling rapid detection and mitigation of false data injection attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If smart grid systems increase communication opportunities between devices, then system functionality and monitoring capability are improved, but vulnerability to cyber-attacks increases
Solution Approach 1:
The patent introduces an intermediary verification mechanism that acts as a mediator between sensor data inputs and system decision-making. This intermediary layer validates data authenticity by checking multiple conditions (data consistency, physical laws compliance, cross-sensor verification) before accepting sensor inputs, thereby blocking malicious data without restricting legitimate communication.
Solution Approach 2:
The system implements feedback mechanisms where the verification results are fed back into the data acceptance process. When false data is detected, the system provides feedback to reject that data source and can adjust verification parameters dynamically, creating a closed-loop security system that learns from detected attacks.
2Measurement precision
If the system accepts all sensor data for monitoring, then measurement coverage is improved, but reliability against false data is worsened
Solution Approach 1:
The patent applies partial verification action by selectively verifying data based on risk assessment. Not all sensor data undergoes the full verification process - the system performs targeted verification on critical parameters or data showing anomalies, while accepting routine data with standard checks. This balances comprehensive monitoring with efficient false data detection.
Solution Approach 2:
The verification process is segmented into multiple independent checks: data format validation, range checking, physical law compliance, cross-sensor consistency, and temporal pattern analysis. Each segment handles a specific aspect of verification, allowing the system to maintain comprehensive monitoring while applying different verification depths to different data streams.
3Difficulty of detecting and measuring
If the system implements comprehensive data verification, then false data detection capability is improved, but system complexity increases
Solution Approach 1:
The verification system uses self-service by leveraging the inherent relationships between sensors and physical laws to perform validation. Instead of requiring external complex verification systems, the sensors themselves provide reference data that can be used to verify other sensors, and the system checks compliance with fundamental physical principles, reducing the need for additional complex verification infrastructure.
4Speed
If the system responds to all fault indications, then response speed is improved, but accuracy of fault detection is worsened
Solution Approach 1:
The system performs preliminary verification of fault indications before triggering responses. Critical checks are performed in advance on incoming data to identify obvious falsifications or inconsistencies. This preliminary action filters out false alarms before they reach the response stage, ensuring that only verified genuine faults trigger system responses, thereby maintaining both speed and accuracy.
Data Source
AI summary
A system for detecting false data injection attacks includes one or more sensors configured to each monitor a component and generate signals representing measurement data associated with the component. The system also includes a fault detection computer device configured to: receive the signals representing measurement data from the one or more sensors, receive a fault indication of a fault associated with the component, generate a profile for the component based on the measurement data, and determine an accuracy of the fault indication based upon the generated profile.


