Autoencoder Neural Network for Unsupervised Anomaly Detection
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Solution Overview
Problem
Current anomaly detection methods using neural networks face challenges in efficiently identifying abnormal signals, particularly in unsupervised learning scenarios where setting a learning goal is difficult, especially in medical anomaly detection where biosignal patterns need to be differentiated from normal patterns.
Innovation Solution
The proposed method employs an unsupervised learning neural network with an autoencoder architecture, including layers of an encoder and a decoder, to extract and process input features, calculate errors, and determine abnormal signals based on a threshold, enabling efficient anomaly detection in various fields such as medical and cyber-intrusion detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If unsupervised learning neural network is used for anomaly detection, then the system can operate without labeled abnormal data, but it becomes difficult to set a learning goal and differentiate abnormal patterns from normal patterns
Solution Approach 1:
The patent uses an autoencoder neural network that learns to copy normal data patterns during training. The encoder-decoder architecture captures the essence of normal biosignal patterns and reconstructs them with high fidelity. When presented with abnormal patterns, the reconstruction quality deteriorates, enabling anomaly detection without requiring labeled abnormal data during training.
Solution Approach 2:
The patent implements a feedback mechanism by comparing the original input signal with the reconstructed output signal from the autoencoder. The difference or error between these signals serves as the anomaly indicator, providing automatic feedback that eliminates the need for manual learning goal setting and enables unsupervised anomaly detection.
2Measurement precision
If traditional anomaly detection methods are used, then the system requires labeled abnormal data for training, but this limits applicability when abnormal data is scarce or unavailable
Solution Approach 1:
The patent inverts the traditional supervised learning approach by training the neural network exclusively on normal data and using reconstruction error as the anomaly indicator. Instead of directly learning to classify anomalies, the system learns to reconstruct normal patterns and identifies deviations from these patterns as anomalies, enabling application in unsupervised scenarios where abnormal data is scarce.
3Measurement precision
If complex neural network architectures are used to improve detection accuracy, then anomaly detection precision increases, but computational complexity and processing time increase
Solution Approach 1:
The patent employs a balanced autoencoder architecture that captures essential biosignal patterns without excessive complexity. The encoder-decoder structure with appropriate layer dimensions achieves sufficient reconstruction accuracy for normal patterns while maintaining computational efficiency. This partial action approach avoids over-engineering the network while still achieving high detection accuracy through the reconstruction error mechanism.
Data Source
AI summary
A method and apparatus for performing anomaly detection by using a neural network are provided. The apparatus is configured to extract input features of an input data signal, obtain output features of the neural network by processing the input features through the neural network, obtain an error based on the input features and the output features, and determine whether the input data signal indicates an abnormal signal based on the error and a threshold.