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

=MULTI-LEVEL MALWARE CLASSIFICATION MACHINE-LEARNING METHOD AND SYSTEM=


==Organization Name==

[[:Category:UAB 360 IT|UAB 360 IT]]


[[Category:UAB 360 IT]]

==Inventor(s)==

[[:Category:Mantas Briliauskas of Vilnius (LT)|Mantas Briliauskas of Vilnius (LT)]][[Category: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.



[[Category:G06F21/56]]
[[Category:G06F21/55]]
[[Category:G06N7/02]]
[[Category:CPC_G06F21/565]]