UAB 360 IT (20240232353). MULTI-LEVEL MALWARE CLASSIFICATION MACHINE-LEARNING METHOD AND SYSTEM simplified abstract
Contents
MULTI-LEVEL MALWARE CLASSIFICATION MACHINE-LEARNING METHOD AND SYSTEM
Organization Name
Inventor(s)
Mantas Briliauskas of Vilnius (LT)
MULTI-LEVEL MALWARE CLASSIFICATION MACHINE-LEARNING METHOD AND SYSTEM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240232353 titled 'MULTI-LEVEL MALWARE CLASSIFICATION MACHINE-LEARNING METHOD AND SYSTEM
Simplified Explanation:
This patent application describes a cyber security method and system for detecting malware using an anti-malware application that employs a fast locality-sensitive hashing evaluation with a vantage-point tree (VPT) structure to identify malicious and non-malicious files.
Key Features and Innovation:
- Utilizes fast locality-sensitive hashing evaluation with a VPT structure for efficient malware detection.
- Prioritizes high confidence identification of malicious and non-malicious files before deeper evaluation.
- Provides a low confidence measure for files that are difficult to classify.
Potential Applications: The technology can be used in various industries such as cybersecurity, IT security, and data protection to enhance malware detection and prevention.
Problems Solved: Addresses the challenge of efficiently detecting and classifying malware files to improve overall cybersecurity measures.
Benefits:
- Enhances the accuracy and speed of malware detection.
- Helps in preventing security breaches and data loss.
- Improves overall cybersecurity posture for organizations.
Commercial Applications: The technology can be applied in antivirus software, network security systems, and data protection tools to enhance malware detection capabilities and improve overall cybersecurity defenses.
Prior Art: Readers can explore prior research on locality-sensitive hashing, vantage-point trees, and malware detection algorithms to understand the background of this technology.
Frequently Updated Research: Stay updated on advancements in malware detection algorithms, cybersecurity technologies, and data protection methods to enhance the effectiveness of this innovation.
Questions about Malware Detection using Locality-Sensitive Hashing and VPT Structure: 1. How does the fast locality-sensitive hashing evaluation with a VPT structure improve malware detection efficiency? 2. What are the potential limitations of using a VPT structure for malware detection?
Original Abstract Submitted
a cyber security method and system for detecting malware via an anti-malware application employing a fast locality-sensitive hashing evaluation using a vantage-point tree (vpt) structure for the indication of malicious files and non-malicious files. the locality-sensitive hashing evaluation using the vpt structure can be performed prior to initiating the deeper, more computationally intensive evaluation and is used to identify with high confidence a scanned file or data object being (i) a malicious file, (ii) a non-malicious file, or a low confidence measure of the two.