Core Concepts
This research explores the application of Anomaly Generative Adversarial Networks (AnoGAN) to detect anomalies in tabular data, addressing challenges in anomaly detection and demonstrating the effectiveness of GAN-based methods compared to traditional techniques.
Abstract
This research paper presents a novel approach to anomaly detection in tabular data using Anomaly Generative Adversarial Networks (AnoGAN). The key highlights are:
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Challenges in Anomaly Detection:
- Distinguishing abnormal patterns from normal ones
- Defining and adapting to evolving "normal" behavior
- Recognizing malicious activities masquerading as normal
- Handling context-dependent anomaly definitions
- Addressing data-related issues like noise and imbalances
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Methodology:
- Data preprocessing, including categorization, normalization, and Gaussian Mixture Model transformation
- Addressing randomness in CT-GAN predictions through a modified softmax gumbel activation
- Optimizing noise vectors using Mean Squared Error (MSE) loss to generate synthetic samples
- Determining an optimal anomaly detection threshold through ROC analysis
- Analyzing individual feature differences between normal and synthetic samples
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Results:
- Achieved an anomaly detection accuracy of around 72% using AnoGAN, outperforming traditional methods like One-Class SVM and K-Nearest Neighbors
- Observed improved performance with extended training, reaching up to 80% accuracy
- Demonstrated exceptional performance (over 85% accuracy) in scenarios with infrequent anomalies
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Future Directions:
- Incorporating domain-specific knowledge to enhance interpretability and performance
- Exploring adaptability to handle categorical variables
- Refining the threshold determination process and extending the framework to dynamic datasets
- Investigating ensemble techniques and hybrid models for further improvements
The research showcases the effectiveness of AnoGAN in detecting anomalies within structured datasets, offering a promising approach to address the complexities inherent in anomaly detection.
Stats
The dataset used in this research covers 14 days of data from 15 Variable Air Volume (VAV) devices at a Google campus, with 3.2% of the 60,425 observations being anomalies.
Quotes
"Anomaly detection, distinct from noise accommodation and removal [17], addresses unwanted noise in data. Noise, defined as irrelevant data phenomena hindering interpretation, requires elimination before analysis. Conversely, noise accommodation shields statistical model estimates from outlier impacts."
"Anomaly detection plays a vital role across various domains by uncovering crucial insights from data anomalies. An unusual network traffic pattern [21] may indicate a security breach, while abnormal MRI scans [22] could signal the presence of tumors. Anomalies in aviation sensors [23] may highlight potential aircraft component issues, and deviations in credit card transactions often signify fraudulent activity."