Industrial Data Pipeline Anomaly Detection Using ML Feature Vectors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Industrial automation environments face security vulnerabilities in data pipelines due to the numerous and diverse communication connections, which are not effectively addressed by existing technologies using machine learning.
Innovation Solution
Integration of machine learning models into industrial automation environments to detect malicious behavior in data pipelines by generating feature vectors representing inputs and outputs, processing machine learning outputs for anomalous behavior, and generating alerts characterizing such behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are integrated into industrial automation environments to detect malicious behavior, then security detection capability is improved, but device complexity increases
Solution Approach 1:
The patent introduces a security component as an intermediary layer between the data pipeline and machine learning engine. This security component generates feature vectors from data pipeline inputs and outputs, serving as a mediator that translates industrial data into formats suitable for machine learning analysis, thereby improving security detection while managing system complexity through modular design
Solution Approach 2:
The system is divided into distinct functional modules: a data pipeline for data processing, a security component for feature extraction and anomaly detection, and a machine learning engine for pattern recognition. This segmentation allows each component to specialize in specific tasks, improving overall security detection capability while making the complex system more manageable and maintainable
2Measurement precision
If feature vectors representing data pipeline inputs and outputs are generated and processed through machine learning, then anomaly detection accuracy is improved, but loss of time increases
Solution Approach 1:
The security component performs preliminary actions by continuously generating feature vectors from data pipeline inputs and outputs before machine learning analysis is required. This pre-processing of data into meaningful feature vectors enables the machine learning engine to quickly detect anomalies without extensive real-time computation, thereby improving detection accuracy while minimizing processing time delays
Solution Approach 2:
The system extracts only the essential features from data pipeline inputs and outputs to create feature vectors for machine learning analysis. By taking out only the most relevant characteristics rather than processing entire data sets, the system achieves high anomaly detection accuracy while significantly reducing the time required for processing
Data Source
AI summary
Various embodiments of the present technology generally relate to industrial automation environments. More specifically, embodiments include systems and methods to detect malicious behavior in an industrial automation environment. In some examples, a security component generates feature vectors that represents inputs and outputs to a data pipeline and supplies the feature vectors to a machine learning engine. The security component processes a machine learning output that indicates when anomalous behavior is detected in the operations of the data pipeline. When anomalous behavior is detected in the operations of the data pipeline, the security component generates and transfers an alert that characterizes the anomalous behavior.