Deep learning method for improved anomaly detection
This technology is a methodological framework that utilizes actor-critic and quantile regression approaches to enhance anomaly detection.
Unmet Need: Reducing data and threshold needs in anomaly detection
Anomaly detection is a crucial task in various fields, including cybersecurity and quality control. Current methods of anomaly detection often rely on large amounts of labeled data and predefined thresholds, which are difficult to obtain and fine-tune. This creates challenges when anomalies are rare or when threshold selection leads to either missed detections or false alarms.
The Technology: Framework for improving anomaly detection with deep learning
This technology uses an actor-critic-based approach and quantile regression to improve anomaly detection. With this method, the detection decision is chosen by the actor’s policy algorithm. Quantile regression helps to better estimate tail probabilities, which factor into the alarm decision-making process, allowing the system to more accurately select the threshold and correct outputs.
Applications:
- Cybersecurity fraud detection
- Industrial equipment monitoring
- Financial market activity monitoring
- Smart vehicle unexpected behavior warnings
Advantages:
- Reduces dependence on labeled data
- Mitigates threshold-setting challenges
- Minimizes training data needed
Lead Inventor:
Patent Information:
Patent Pending
Related Publications:
Tech Ventures Reference:
IR CU24165
Licensing Contact: Greg Maskel