Anomaly Detection Using Convolutional Auto-Encoder
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Solution Overview
Problem
Current anomaly detection methods in IT infrastructure face challenges in real-time analysis, unsupervised learning, and adapting to changing data patterns, particularly in vast and complex time series data, where manual adaptation is impractical and reliable rule generation is difficult.
Innovation Solution
Anomaly detection system utilizing a convolutional auto-encoder in an unsupervised learning scheme, generating a deep learning model based on past normal data, automatically calculating thresholds, and applying statistical models like 3sigma rules and coefficient of variance to detect outliers and anomalies in real-time, while continuously retraining the model to adapt to changing patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual adaptation methods are used for anomaly detection in time series data, then the system can handle simple data patterns, but it becomes impractical and inefficient for vast and complex time series data in real-time
Solution Approach 1:
The system performs self-service by automatically learning from time series data through unsupervised deep learning. The convolutional autoencoder model autonomously identifies patterns and anomalies without manual rule configuration, enabling the system to handle complex time series data independently and scale to real-time processing requirements
Solution Approach 2:
Manual mechanical rule-based detection is replaced with an intelligent deep learning system. The convolutional autoencoder automatically learns complex patterns from data, substituting the need for manual rule creation and adaptation with an automated neural network-based detection mechanism
2Ease of manufacture
If rule-based anomaly detection is used, then the detection process is simple to implement, but reliable rule generation is difficult for complex time series patterns
Solution Approach 1:
The system transforms the approach from fixed rule parameters to learned parameters through deep learning. The convolutional autoencoder automatically adjusts its internal parameters during training to capture complex time series patterns, making the detection system both reliable for complex patterns and simple to implement as a unified model-based approach
3Adaptability or versatility
If a deep learning model is trained on past normal data to detect anomalies, then the system can automatically adapt to changing data patterns through continuous retraining, but the model complexity and training requirements increase
Solution Approach 1:
The system implements dynamic adaptation through continuous retraining of the convolutional autoencoder model. As new time series data becomes available, the model is retrained to learn evolving patterns, making the anomaly detection system dynamically adaptive to changing conditions while maintaining a structured training approach
Solution Approach 2:
The system performs preliminary training on historical normal data before deployment, establishing a baseline model that captures normal patterns. This preliminary action prepares the model for real-time anomaly detection, and subsequent retraining updates the model as needed without requiring complete retraining from scratch
Data Source
AI summary
An anomaly detection method includes collecting and preprocessing time series data every preset detection cycle; detecting an anomaly in time series data preprocessed for a current detection cycle using a deep learning model trained with an unsupervised learning scheme using features of time series data of a previous detection cycle; retraining the deep learning model by further using the time series data preprocessed for at least one detection cycle included in the current learning cycle; and detecting an anomaly in time series data collected and preprocessed for a detection cycle after the current learning cycle using the retrained deep learning model.