Cybersecurity Clustering System for Threat Analysis
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Solution Overview
Problem
Current cybersecurity systems face inefficiencies in processing large numbers of reported threat communications, leading to unnecessary processing loads and delays, particularly due to the inability to automatically respond to phishing attacks and scale responses globally.
Innovation Solution
A system and method utilizing machine learning and artificial intelligence, combined with large-scale human input, for clustering and threat scoring, which identifies potential threat clusters, determines threat levels, and performs corrective actions to manage communications effectively, thereby reducing the risk of cyber-attacks and the need for dedicated cybersecurity teams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all reported threat communications are processed individually by SOCs, then thorough threat analysis is achieved, but processing time and operational load increase significantly
Solution Approach 1:
The patent segments the large volume of reported threat communications into smaller clusters based on shared attributes (sender domain, recipient domain, message characteristics). This segmentation allows SOCs to analyze one representative communication per cluster rather than every individual report, maintaining thorough analysis accuracy while dramatically reducing processing time and operational load.
Solution Approach 2:
The patent merges multiple similar threat communications into clusters based on shared attributes. By combining communications with identical or similar characteristics (same sender domain, recipient domain, message type), the system creates representative clusters that capture the essence of multiple threats without requiring individual analysis of each communication, thus reducing processing time while preserving analysis quality.
2Reliability
If manual processing of threat communications is used, then detailed human expertise is applied, but response speed and scalability are limited
Solution Approach 1:
The patent introduces an automated clustering system as an intermediary between threat report collection and SOC analysis. This intermediary automatically groups similar threats by attributes (sender domain, recipient domain, message characteristics) before presenting clustered results to human analysts, combining automated processing speed with human expertise for reliable threat detection and rapid response.
Solution Approach 2:
The patent replaces manual sorting and initial analysis of individual threat communications with an automated computational clustering system. This system uses algorithms to group threats by shared attributes, substituting mechanical human effort with automated processing while preserving human expertise for final threat assessment and response decisions.
3Ease of operation
If each organization handles threats individually, then localized response is achieved, but global threat response capability is insufficient
Solution Approach 1:
The patent creates a multi-functional system that operates at both local and global levels. The clustering mechanism works universally across different organizations and domains, grouping threats by shared attributes regardless of source or target. This universal approach enables localized response capabilities within each organization while simultaneously providing global threat response capability through shared clustering patterns and coordinated actions across multiple organizations.
Data Source
AI summary
A system for providing cyber security, having a server arrangement communicatively coupled to a plurality of user devices. The server arrangement configured to receive communications from communication sources; forward received communications to the user devices; receive feedbacks, associated with the forwarded communications from one or more user devices to identify potential threat communications; analyze the received communications to generate a plurality of clusters based on one or more shared attributes associated with the received communications; identify one or more clusters as potential threat clusters, including the at least one potential threat communication; analyze at least one threat indicator communication, selected from each of the identified one or more potential threat clusters, to determine a threat level associated therewith; and classify the one or more potential threat clusters as threat clusters, if the threat level of the at least one threat indicator communication is above a threshold threat level.