International business machines corporation (20240281522). ADDRESSING STRUCTURED FALSE POSITIVES DURING ASSET ANOMALY DETECTION simplified abstract
Contents
ADDRESSING STRUCTURED FALSE POSITIVES DURING ASSET ANOMALY DETECTION
Organization Name
international business machines corporation
Inventor(s)
Yu-Jin Chen of New Taipei City (TW)
Yu-Chi Tang of New Taipei City (TW)
Shih Hsuan Lee of Zhuangwei (TW)
ADDRESSING STRUCTURED FALSE POSITIVES DURING ASSET ANOMALY DETECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240281522 titled 'ADDRESSING STRUCTURED FALSE POSITIVES DURING ASSET ANOMALY DETECTION
The abstract describes techniques for addressing structured false positives in detecting asset anomalies in a computing environment using machine learning models.
- Applying an anomaly detection machine learning model to assets to determine anomaly assets based on anomaly risk scores.
- Calculating structured false positive scores for anomaly assets during a current time window.
- Retraining the machine learning model when anomaly assets exceed a structured false positive threshold.
Potential Applications: - Enhancing cybersecurity measures in organizations. - Improving fraud detection systems in financial institutions.
Problems Solved: - Reducing false positives in anomaly detection. - Enhancing the accuracy of identifying asset anomalies.
Benefits: - Increased efficiency in anomaly detection. - Enhanced security measures in computing environments.
Commercial Applications: Title: "Enhancing Cybersecurity Measures with Advanced Anomaly Detection Technology" This technology can be used in various industries such as finance, healthcare, and e-commerce to improve security measures and prevent fraudulent activities.
Questions about the technology: 1. How does this technology improve the accuracy of anomaly detection in computing environments? 2. What are the potential implications of reducing false positives in asset anomaly detection?
Original Abstract Submitted
techniques are described with regard to addressing structured false positives in the context of detecting asset anomalies in a computing environment. an associated computer-implemented method includes applying an anomaly detection machine learning model to each of a plurality of assets in order to determine a plurality of anomaly assets among the plurality of assets. the plurality of anomaly assets are determined based upon a model anomaly risk score calculated for each of the plurality of assets consequent to asset event data analysis. the method further includes calculating a structured false positive score for each of the plurality of anomaly assets during a current structured false positive time window. the method further includes retraining the anomaly detection machine learning model responsive to determining that a threshold value of anomaly assets among the plurality of anomaly assets have a structured false positive score exceeding a structured false positive threshold value.
