UAB 360 IT (20240232349). MULTI-LEVEL MALWARE CLASSIFICATION MACHINE-LEARNING METHOD AND SYSTEM simplified abstract

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MULTI-LEVEL MALWARE CLASSIFICATION MACHINE-LEARNING METHOD AND SYSTEM

Organization Name

UAB 360 IT

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 20240232349 titled 'MULTI-LEVEL MALWARE CLASSIFICATION MACHINE-LEARNING METHOD AND SYSTEM

Simplified Explanation:

The 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 files and non-malicious files.

Key Features and Innovation:

  • Utilizes fast locality-sensitive hashing evaluation with a VPT structure for efficient malware detection.
  • Helps in indicating whether a scanned file is malicious, non-malicious, or a low confidence measure of both.
  • Performs the hashing evaluation before deeper, more computationally intensive evaluations.

Potential Applications: This technology can be used in various cyber security applications, including antivirus software, network security systems, and malware detection tools.

Problems Solved:

  • Efficiently detects malware in scanned files.
  • Provides high confidence measures for identifying malicious files.

Benefits:

  • Enhances cyber security measures.
  • Improves malware detection accuracy.
  • Reduces false positives in identifying malicious files.

Commercial Applications: Potential commercial applications include integrating this technology into antivirus software, network security solutions, and malware detection tools to enhance their effectiveness and accuracy.

Prior Art: Readers can explore prior research on locality-sensitive hashing, Vantage-Point trees, and malware detection methods to understand the background of this technology.

Frequently Updated Research: Stay updated on advancements in cyber security, malware detection techniques, and hashing algorithms to enhance the effectiveness of this technology.

Questions about Cyber Security Technology: 1. How does the fast locality-sensitive hashing evaluation improve malware detection efficiency? 2. What are the potential limitations of using a Vantage-Point tree structure in cyber security applications?


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 a malicious file, a non-malicious file, or a low confidence measure of the two.

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