GOOGLE LLC (20240348629). System and Method for Automatically Associating Cybersecutiry Intelligence to Cyberthreat Actors simplified abstract
Contents
System and Method for Automatically Associating Cybersecutiry Intelligence to Cyberthreat Actors
Organization Name
Inventor(s)
Matthew Berninger of Denver CO (US)
Barry Vengerik of Montclair NJ (US)
System and Method for Automatically Associating Cybersecutiry Intelligence to Cyberthreat Actors - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240348629 titled 'System and Method for Automatically Associating Cybersecutiry Intelligence to Cyberthreat Actors
The computerized method described in the abstract is for associating cyberthreat actor groups responsible for different cyberthreats. It involves generating a similarity matrix based on content from received clusters of cybersecurity information, with each cluster assumed to be associated with a cyberthreat. The similarity matrix is composed via an optimized equation combining separate similarity metrics, each representing a level of correlation between clusters of cybersecurity information. In response to queries directed to the similarity matrix, a listing of a subset of clusters with a greater likelihood of being associated with cyberthreats caused by the same cyberthreat actor group is generated.
- Utilizes a similarity matrix to associate cyberthreat actor groups responsible for different cyberthreats
- Generates the similarity matrix based on content from clusters of cybersecurity information
- Each cluster is assumed to be associated with a cyberthreat
- Composes the similarity matrix using an optimized equation combining separate similarity metrics
- Provides a listing of clusters with a higher likelihood of being associated with cyberthreats caused by the same cyberthreat actor group in response to queries
Potential Applications: - Cybersecurity threat analysis and attribution - Incident response and threat intelligence - Identifying patterns and trends in cyberattacks
Problems Solved: - Attribution of cyberthreats to specific actor groups - Enhancing cybersecurity defenses by understanding threat actor behaviors - Streamlining threat analysis and response processes
Benefits: - Improved accuracy in identifying cyberthreat actors - Enhanced cybersecurity posture through targeted threat response - Efficient utilization of cybersecurity resources
Commercial Applications: Title: Cyber Threat Attribution and Analysis Technology This technology can be used by cybersecurity firms, government agencies, and organizations to enhance their threat intelligence capabilities, improve incident response, and strengthen overall cybersecurity defenses. The market implications include increased demand for advanced threat analysis tools and services in the cybersecurity industry.
Prior Art: Readers can explore prior research on threat attribution, cybersecurity analytics, and threat intelligence platforms to gain a deeper understanding of the existing technologies in this field.
Frequently Updated Research: Researchers are continually developing new algorithms and methodologies to improve cyber threat attribution and analysis techniques. Stay updated on the latest advancements in threat intelligence and cybersecurity analytics to leverage cutting-edge technologies in this domain.
Questions about Cyber Threat Attribution and Analysis Technology: 1. How does this technology differentiate between various cyberthreat actor groups? 2. What are the key factors considered when generating the similarity matrix for cyberthreat attribution?
Original Abstract Submitted
a computerized method for associating cyberthreat actor groups responsible for different cyberthreats is described. the method involves generating a similarity matrix based on content from received clusters of cybersecurity information. each received cluster of cybersecurity information is assumed to be associated with a cyberthreat. the similarity matrix is composed via an optimized equation combining separate similarity metrics, where each similarity metric of the plurality of similarity metrics represents a level of correlation between at least two clusters of cybersecurity information, with respect to a particular aspect of operations described in the clusters. the method further involves that, in response to queries directed to the similarity matrix, generating a listing of a subset of the clusters of cybersecurity information having a greater likelihood of being associated with cyberthreats caused by the same cyberthreat actor group.
