18075824. EXPERT-OPTIMAL CORRELATION: CONTAMINATION FACTOR IDENTIFICATION FOR UNSUPERVISED ANOMALY DETECTION simplified abstract (Oracle International Corporation)

EXPERT-OPTIMAL CORRELATION: CONTAMINATION FACTOR IDENTIFICATION FOR UNSUPERVISED ANOMALY DETECTION

Organization Name

Oracle International Corporation

Inventor(s)

Yasha Pushak of Vancouver (CA)

Constantin Le Clei of Zurich (CH)

Fatjon Zogaj of Zurich (CH)

Hesam Fathi Moghadam of Sunnyvale CA (US)

Sungpack Hong of Palo Alto CA (US)

Hassan Chafi of San Mateo CA (US)

EXPERT-OPTIMAL CORRELATION: CONTAMINATION FACTOR IDENTIFICATION FOR UNSUPERVISED ANOMALY DETECTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 18075824 titled 'EXPERT-OPTIMAL CORRELATION: CONTAMINATION FACTOR IDENTIFICATION FOR UNSUPERVISED ANOMALY DETECTION

Simplified Explanation

The abstract describes a method for detecting inaccuracies in anomaly detectors in a computer system by comparing anomaly scores and contamination factors.

• Anomaly detectors in a computer system infer anomaly scores for tuples.
• Synthetic labels are generated for each tuple, indicating the anomaly detector, anomaly score, contamination factor, and binary class of the anomaly score.
• Similarity scores are calculated between different anomaly detectors for each contamination factor.
• A combined similarity score is calculated based on the similarity scores for each contamination factor.
• The computer detects inaccuracies in an anomaly detector based on the contamination factor with the highest combined similarity score.

Potential Applications

This technology could be applied in various industries where anomaly detection is crucial, such as cybersecurity, fraud detection, and quality control in manufacturing.

Problems Solved

1. Identifying inaccuracies in anomaly detectors. 2. Improving the overall accuracy of anomaly detection systems.

Benefits

1. Enhanced anomaly detection accuracy. 2. Reduction in false positives and false negatives. 3. Improved system reliability and performance.

Potential Commercial Applications

Optimizing anomaly detection systems for cybersecurity firms. SEO Optimized Title: "Enhancing Anomaly Detection Systems for Cybersecurity Firms"

Possible Prior Art

One possible prior art could be the use of machine learning algorithms for anomaly detection in various industries. However, the specific method described in this patent application for detecting inaccuracies in anomaly detectors may be a novel approach.

Unanswered Questions

=== How does this method compare to traditional anomaly detection techniques? This article does not provide a direct comparison to traditional anomaly detection techniques, leaving the reader to wonder about the specific advantages of this new method over existing ones.

=== What are the potential limitations of this method in real-world applications? The article does not address any potential limitations or challenges that may arise when implementing this method in practical scenarios, leaving room for uncertainty regarding its effectiveness in real-world settings.

Original Abstract Submitted

In a computer, each of multiple anomaly detectors infers an anomaly score for each of many tuples. For each tuple, a synthetic label is generated that indicates for each anomaly detector: the anomaly detector, the anomaly score inferred by the anomaly detector for the tuple and, for each of multiple contamination factors, the contamination factor and, based on the contamination factor, a binary class of the anomaly score. For each particular anomaly detector excluding a best anomaly detector, a similarity score is measured for each contamination factor. The similarity score indicates how similar, between the particular anomaly detector and the best anomaly detector, are the binary classes of labels with that contamination factor. For each contamination factor, a combined similarity score is calculated based on the similarity scores for the contamination factor. Based on a contamination factor that has the highest combined similarity score, the computer detects that an additional anomaly detector is inaccurate.