ML-Driven Cyber Attack Assessment for System Risk Accuracy
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Solution Overview
Problem
Traditional methods for assessing the risk of cyber attacks are time-consuming and prone to human errors, lacking comprehensive and efficient methods for assessing the risk of cyber attacks, which are not addressed in the traditional methods, and existing technologies fail to automate the assessment of cyber attacks on the system, and existing technologies fail to address the need for automation and human errors in the risk of cyber attacks.
Innovation Solution
A method using a machine learning agent to simulate cyber attacks, evaluate the results, and determine the risk of cyber attacks on a system, reducing human intervention and errors, and automating the assessment process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional manual methods are used to simulate and assess cyber attacks, then the assessment can be performed with human judgment and flexibility, but the process becomes highly time-consuming and prone to human errors
Solution Approach 1:
The patent replaces the manual mechanical assessment process with an automated machine learning-based system. The machine learning agent autonomously simulates cyber attacks, evaluates system responses, and generates risk assessments without human intervention, thereby eliminating human errors and significantly reducing assessment time while maintaining or improving accuracy through consistent automated evaluation criteria
Solution Approach 2:
The system enables self-service by allowing the machine learning agent to independently perform the entire cyber attack assessment process. The agent autonomously generates attack scenarios, executes simulations, analyzes results, and produces assessments without requiring human test engineers, making the system self-sufficient and dramatically improving efficiency
2Adaptability or versatility
If manual test engineer assessment is used, then subjective criteria can be applied, but the assessment is subject to errors of human judgment such as distortions, biases, or random errors
Solution Approach 1:
The patent replaces human subjective judgment with an automated machine learning system that applies consistent, objective evaluation criteria. The machine learning agent eliminates human biases and errors by using standardized algorithms and metrics to assess cyber attack risks, ensuring precise and reproducible measurements across different assessments while maintaining adaptability through configurable assessment parameters
3Ease of operation
If traditional approaches are used to create risk assessment, then human test engineers can simulate cyber attack scenarios, but the process lacks automation and requires significant human resources
Solution Approach 1:
The system implements self-service by enabling the machine learning agent to autonomously perform all assessment tasks without human intervention. The agent automatically simulates cyber attacks, evaluates system responses, and generates risk assessments, thereby simplifying the operation for users while dramatically increasing productivity through automated high-volume processing of attack scenarios
Solution Approach 2:
The patent substitutes manual human operations with an automated machine learning system. The machine learning agent performs all previously manual tasks including attack simulation, result evaluation, and report generation, making the process easier to operate while significantly improving productivity through continuous automated execution without human resource constraints
Data Source
AI summary
A method for determining correctness and/or for generating an assessment of the risk of cyber attacks on a particular system. The method includes receiving a request to carry out cyber attack(s) on the system and invoking a machine learning agent. The machine learning agent accesses a generative machine learning model. The method further includes carrying out one or more cyber attacks on the system using the machine learning agent in response to the request, evaluating the results of the one or more carried out cyber attacks and determining, based on a finding of the step of evaluating the result, whether a predetermined assessment of the risk of cyber attacks on the particular system is correct or generating, based on a finding of the step of evaluating the result, an assessment of the risk of cyber attacks on the particular system.