17938771. SYSTEM AND METHOD FOR MEMORY-LESS ANOMALY DETECTION USING ANOMALY LEVELS simplified abstract (Dell Products L.P.)

SYSTEM AND METHOD FOR MEMORY-LESS ANOMALY DETECTION USING ANOMALY LEVELS

Organization Name

Dell Products L.P.

Inventor(s)

OFIR Ezrielev of Beer Sheva (IL)

NADAV Azaria of Beer Sheva (IL)

AVITAN Gefen of Tel Aviv (IL)

SYSTEM AND METHOD FOR MEMORY-LESS ANOMALY DETECTION USING ANOMALY LEVELS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17938771 titled 'SYSTEM AND METHOD FOR MEMORY-LESS ANOMALY DETECTION USING ANOMALY LEVELS

Simplified Explanation

The abstract describes methods and systems for anomaly detection in a distributed environment, involving an anomaly detector and data collectors. The detector identifies anomalies in data and classifies them based on magnitudes using an inference model. Different magnitudes trigger different actions in response to anomalies, with the model requiring re-training using data collected by the data collectors. After detection and re-training, the data is discarded.

• Anomaly detection system in a distributed environment
• Anomaly detector identifies and classifies anomalies based on magnitudes using an inference model
• Different magnitudes trigger different actions in response to anomalies
• Inference model requires re-training using data collected by data collectors
• Data is discarded after detection and re-training

Potential Applications

Anomaly detection systems like the one described can be used in various industries such as cybersecurity, network monitoring, fraud detection, and predictive maintenance.

Problems Solved

This technology helps in early detection of anomalies in large datasets, enabling proactive measures to be taken to prevent potential issues or threats.

Benefits

The benefits of this technology include improved data security, reduced downtime, cost savings through predictive maintenance, and enhanced decision-making based on real-time anomaly detection.

Potential Commercial Applications

Potential commercial applications of this technology include offering anomaly detection services to businesses, developing custom anomaly detection solutions for specific industries, and integrating anomaly detection capabilities into existing software systems.

Possible Prior Art

One possible prior art in anomaly detection is the use of statistical methods and machine learning algorithms to detect anomalies in data streams. Another prior art could be the use of rule-based systems for anomaly detection in specific domains.

How does the system handle false positives in anomaly detection?

The abstract does not mention how the system deals with false positives in anomaly detection. It would be important to understand the approach taken to minimize false alarms and ensure accurate anomaly detection.

What is the scalability of the system in a distributed environment with a large volume of data?

The scalability of the system in handling a large volume of data in a distributed environment is not discussed in the abstract. Understanding how the system can efficiently process and analyze massive amounts of data would be crucial for its practical implementation.

Original Abstract Submitted

Methods and systems for anomaly detection in a distributed environment are disclosed. To manage anomaly detection, a system may include an anomaly detector and one or more data collectors. The anomaly detector may detect anomalies in data and classify the anomalies based on magnitudes of anomalies using an inference model. Different magnitudes of anomalies may be keyed to different action sets in response to the presence of anomalies in data. To perform anomaly detection, the inference model may require re-training. Data collected from the one or more data collectors may be used to re-train the inference model as needed. Following anomaly detection and/or inference model re-training, the data may be discarded to remove the data from the anomaly detector.