Simulated Cybersecurity Attack Difficulty Modeling
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Solution Overview
Problem
Existing cybersecurity training methods struggle to effectively prepare users against evolving cyber threats, as they often focus on outdated information and fail to adapt quickly to new attack methods.
Innovation Solution
A flexible and automated system that determines the most risky cybersecurity attacks for an organization, generates training and testing materials based on these threats, and provides benchmark information for comparing performance with other organizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If cybersecurity training focuses on comprehensive attack types, then training completeness is improved, but training relevance and user engagement deteriorate due to outdated or low-risk information
Solution Approach 1:
The system dynamically changes the parameters of training content selection by using machine learning models to predict attack failure rates based on organizational attributes. This allows the system to filter and select only the most relevant training materials, transforming static comprehensive training into dynamic, context-aware training that adapts to organizational risk profiles.
Solution Approach 2:
The system incorporates feedback loops where predicted failure rates from machine learning models are used to continuously refine training content selection. Organizational responses to simulated attacks provide feedback that updates the models, enabling progressive improvement in training relevance over time.
2Ease of manufacture
If traditional cybersecurity training methods are used, then implementation simplicity is maintained, but training effectiveness deteriorates against evolving attack methods
Solution Approach 1:
The system performs self-service through automated machine learning model execution and predictive analysis. The platform automatically predicts failure rates, selects training content, and generates simulations without requiring manual intervention, thereby maintaining implementation simplicity while dramatically improving effectiveness through intelligent automation.
Solution Approach 2:
The patent replaces manual training content selection and attack simulation mechanisms with automated machine learning models and computational algorithms. This substitution eliminates the need for human curators to manually select training materials, thereby simplifying implementation while improving reliability through data-driven predictions.
3Quantity of substance
If users are trained on all attack types, then training comprehensiveness is improved, but user burden and attention span deteriorate
Solution Approach 1:
The system extracts and isolates only the most critical training content by using machine learning models to predict which attack types pose the highest failure rates for each organization. This extraction process removes irrelevant or low-priority training materials, leaving only the essential content that users need to address their specific risk profiles.
Solution Approach 2:
The system applies local quality by customizing training content at the organizational level based on predicted failure rates and risk profiles. Rather than applying uniform training to all users, the system tailors training focus to local organizational characteristics, ensuring that each organization receives training relevant to its specific threat landscape.
Data Source
AI summary
Aspects of the disclosure relate to providing training and information based on simulated cybersecurity attack difficulty. A computing platform may retrieve data associated with a plurality of attack templates for simulating cybersecurity attacks. Subsequently, the computing platform may use one or more models to compute a predicted failure rate for each template of the plurality of attack templates in order to yield a plurality of predicted failure rates for an organization. Based on the plurality of predicted failure rates, the computing platform may use one or more of the plurality of attack templates to configure a simulated cybersecurity attack on the organization. Then, the computing platform may send, via the communication interface, to an administrator user device associated with the organization, information about the simulated cybersecurity attack and may execute the simulated cybersecurity attack.