SENTINELONE, INC. (20240241956). CLASSIFYING CYBERSECURITY THREATS USING MACHINE LEARNING ON NON-EUCLIDEAN DATA simplified abstract
Contents
CLASSIFYING CYBERSECURITY THREATS USING MACHINE LEARNING ON NON-EUCLIDEAN DATA
Organization Name
Inventor(s)
Dean Langsam of Ness Ziona (IL)
CLASSIFYING CYBERSECURITY THREATS USING MACHINE LEARNING ON NON-EUCLIDEAN DATA - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240241956 titled 'CLASSIFYING CYBERSECURITY THREATS USING MACHINE LEARNING ON NON-EUCLIDEAN DATA
The abstract of the patent application describes systems, methods, and devices for cybersecurity that utilize machine learning to analyze behavior-based data, detect malware, and identify threats in real-time.
- Machine learning approaches are employed to better understand complex relationships and sequencing associated with behavior-based data.
- Machine learning is effectively applied for behavior-based analysis, malware detection, and real-time threat identification and classification.
Potential Applications: - Cybersecurity systems for businesses and organizations - Malware detection software for personal computers and networks - Threat identification tools for government agencies and security firms
Problems Solved: - Enhancing cybersecurity measures with advanced machine learning techniques - Improving malware detection and threat identification capabilities - Providing real-time analysis of behavior-based data for proactive security measures
Benefits: - Increased accuracy in detecting and classifying threats - Real-time response to cybersecurity incidents - Enhanced protection against malware and cyber attacks
Commercial Applications: Title: Advanced Cybersecurity Systems with Machine Learning This technology can be used in various industries such as finance, healthcare, and government to strengthen cybersecurity measures and protect sensitive data from cyber threats.
Questions about cybersecurity systems with machine learning: 1. How does machine learning improve cybersecurity measures? Machine learning enhances cybersecurity by analyzing behavior-based data and detecting threats in real-time. 2. What are the potential applications of machine learning in cybersecurity? Machine learning can be used for malware detection, threat identification, and behavior-based analysis in cybersecurity systems.
Original Abstract Submitted
systems, methods, and devices for cybersecurity are disclosed herein that can employ machine learning approaches with a better understanding of the complex relationships and sequencing associated with behavior-based data, and that can effectively apply machine learning for behavior-based analysis, malware detection, and identifying and classifying threats in real-time.
