UAB 360 IT (20240232355). 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 20240232355 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 files and non-malicious files with high confidence.

Key Features and Innovation:

  • Utilizes fast locality-sensitive hashing evaluation with a VPT structure for malware detection
  • Enables high confidence identification of malicious and non-malicious files
  • Performs preliminary evaluation before deeper, more computationally intensive analysis

Potential Applications: This technology can be applied in various industries such as cybersecurity, IT security, and data protection to enhance malware detection capabilities.

Problems Solved:

  • Efficiently detects malware with high confidence
  • Reduces false positives and false negatives in malware detection
  • Improves overall cybersecurity measures

Benefits:

  • Enhanced malware detection capabilities
  • Improved cybersecurity defenses
  • Reduced risk of malware infections

Commercial Applications: The technology can be utilized by cybersecurity companies, IT departments, and organizations handling sensitive data to strengthen their security measures and protect against malware threats.

Prior Art: Readers can explore prior art related to fast locality-sensitive hashing, VPT structures, and malware detection algorithms in the field of cybersecurity.

Frequently Updated Research: Stay updated on advancements in fast locality-sensitive hashing, VPT structures, and malware detection algorithms to enhance cybersecurity measures and protect against evolving threats.

Questions about Cyber Security Method and System for Detecting Malware: 1. How does the fast locality-sensitive hashing evaluation with a VPT structure improve malware detection? 2. What are the potential implications of this technology on the cybersecurity industry?


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.

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