18120773. DETECTION AND INTERPRETATION OF LOG ANOMALIES simplified abstract (Adobe Inc.)
Contents
DETECTION AND INTERPRETATION OF LOG ANOMALIES
Organization Name
Inventor(s)
Jaeho Bang of Hwaseong-si (KR)
Sungchul Kim of San Jose CA (US)
Ryan A. Rossi of San Jose CA (US)
Handong Zhao of Cupertino CA (US)
DETECTION AND INTERPRETATION OF LOG ANOMALIES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18120773 titled 'DETECTION AND INTERPRETATION OF LOG ANOMALIES
The abstract of this patent application describes a system for detecting and interpreting log anomalies using a machine learning model trained on training data.
- The computing device implements an anomaly system to process input data describing two-dimensional representations of log templates and timestamps.
- The machine learning model is used to detect anomalies in the two-dimensional representations of log templates and timestamps.
- An indication of an interpretation of the log anomaly is generated for display in a user interface based on a log template included in the two-dimensional representation.
Potential Applications: - Cybersecurity: Detecting abnormal log patterns that may indicate security breaches. - IT Operations: Identifying irregularities in system logs to improve performance and troubleshoot issues. - Fraud Detection: Flagging suspicious activities in transaction logs for further investigation.
Problems Solved: - Automating the detection of log anomalies to improve efficiency and accuracy. - Providing insights into the root causes of anomalies for better decision-making. - Enhancing the overall security and reliability of systems through proactive monitoring.
Benefits: - Early detection of potential security threats. - Improved system performance and reliability. - Enhanced decision-making based on detailed anomaly interpretations.
Commercial Applications: Title: "Advanced Log Anomaly Detection System for Enhanced Cybersecurity" This technology can be utilized by cybersecurity firms, IT departments, and financial institutions to enhance their monitoring and detection capabilities, ultimately improving overall system security and performance.
Questions about the technology: 1. How does the machine learning model differentiate between normal and anomalous log patterns? 2. What are the key factors that contribute to the accuracy of anomaly detection in this system?
Original Abstract Submitted
In implementations of systems for detection and interpretation of log anomalies, a computing device implements an anomaly system to receive input data describing a two-dimensional representation of log templates and timestamps. The anomaly system processes the input data using a machine learning model trained on training data to detect anomalies in two-dimensional representations of log templates and timestamps. A log anomaly is detected in the two-dimensional representation using the machine learning model based on processing the input data. The anomaly system generates an indication of an interpretation of the log anomaly for display in a user interface based on a log template included in the two-dimensional representation.
