Cyber-Attack Frequency Tracking System
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Solution Overview
Problem
The reliability and accuracy of data regarding cyber-attack frequencies in enterprise computing environments are lacking, leading to uncertainty in resource allocation and confusion among threat intelligence professionals due to multiple and complex industry frameworks.
Innovation Solution
A method and system that automatically receive and analyze data on cyber-attack attempts, determining attack types, frequencies, and contact frequencies, calculating a cyber-attack event frequency half-life value, and presenting real-time visual representations to simplify and unify data formats for improved threat intelligence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple industry frameworks are used to analyze cyber-attacks, then comprehensive threat coverage is improved, but data complexity and analysis difficulty increase
Solution Approach 1:
The patent combines multiple industry frameworks (CAPEC, CWE, ATT&CK, OWASP) into a unified data structure that standardizes cyber-attack information. This merging approach maintains comprehensive threat coverage from all frameworks while presenting a single standardized format for analysis, thereby improving reliability without proportionally increasing complexity.
Solution Approach 2:
The patent creates a universal data structure that can accommodate information from multiple different frameworks simultaneously. This multi-functional structure serves as a common interface for various threat intelligence sources, allowing the system to process diverse framework data through a single standardized pathway, thus improving comprehensive coverage without requiring separate analysis processes for each framework.
2Measurement precision
If manual analysis of cyber-attack data is performed, then data accuracy can be verified, but resource consumption and time requirements increase
Solution Approach 1:
The patent implements automated processing of cyber-attack data that performs verification and analysis without requiring manual intervention. The system automatically processes attack frequency data, calculates metrics, and generates reports, thereby maintaining measurement precision through systematic processing while dramatically improving productivity by eliminating manual analysis bottlenecks.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational systems. The automated system processes large volumes of attack data, performs calculations, and generates insights without human intervention, thus maintaining accuracy through consistent algorithmic processing while significantly increasing analysis speed and reducing resource consumption compared to manual methods.
3Reliability
If detailed attack data is collected and analyzed, then threat assessment accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent pre-structures data collection processes and establishes standardized data formats in advance. By preparing the data infrastructure beforehand with predefined schemas and processing pipelines, the system can rapidly process detailed attack data when it arrives, maintaining high threat assessment accuracy without excessive processing delays.
Solution Approach 2:
The patent implements dynamic parameter adjustment in the data processing system, allowing it to optimize processing speed and depth based on current threat levels and system load. This enables the system to maintain high-quality threat intelligence by adjusting analysis parameters in real-time, balancing detailed examination with processing efficiency to minimize time loss.
Data Source
AI summary
At least some embodiments are directed to a computer-based cyber-attack frequency tracking system that determines types and frequencies of cyber-attacks. In at least some embodiments, the method of a cyber-attack frequency tracking system may operate a processor in an enterprise computing environment for automatically conducting a process that comprises receiving, a plurality of data values that represent a plurality of cyber-attacks. Determining cyber-attack types, and then determining the frequency of attempts and contacts with assets. After that determining likelihood values. Aggregating these determinations to produce a quantifiable value of a likelihood values of each of the plurality of cyber-attack types.