AI Cybersecurity Strategy Analysis via Directed Graph Simulation

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Solution Overview

Problem

Modern networked systems face significant challenges in defending against evolving cybersecurity threats due to their complexity, with current methodologies being largely reactive and unable to predict and counter new attack strategies effectively.

Innovation Solution

A system and method utilizing machine learning algorithms to simulate attack and defense strategies on a model of the network created using a directed graph, allowing for predictive analysis and cost-benefit-based recommendations for cybersecurity improvements.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If current reactive cybersecurity methodologies are used, then implementation simplicity is maintained, but the ability to predict and counter new attack strategies deteriorates

Engineering Contradiction:

Improveability to predict and counter attack strategiesVSAvoidsystem complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by using machine learning algorithms to simulate and predict future attack strategies before they occur in reality. The simulation environment proactively tests defensive measures against predicted attacks, allowing organizations to prepare defenses in advance rather than reacting after attacks occur.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates a virtual copy of the network environment using a directed graph model that replicates the structure and relationships of the actual network. This copy is used for simulations, allowing analysis of attack strategies and defensive measures without affecting the real system, thereby managing complexity through virtualization.

Inventive Principle:

Principle #26Copying

2Reliability

If machine learning simulations are implemented, then predictive cybersecurity analysis is improved, but computational resources and time consumption increase

Engineering Contradiction:

Improvepredictive analysis capabilityVSAvoidsimulation time

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system applies partial action by focusing simulations on specific attack vectors and network segments rather than exhaustively analyzing every possible attack scenario. The machine learning models are trained on representative datasets that capture the essence of attack patterns without requiring complete coverage of all potential threats, reducing computation time while maintaining predictive accuracy.

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If iterative simulations are performed, then defensive strategy effectiveness is improved, but processing speed deteriorates

Engineering Contradiction:

Improvedefensive strategy effectivenessVSAvoidprocessing speed

Core Design Contradiction:

ReliabilityVSProductivity

Solution Approach 1:

The system implements periodic action by conducting iterative simulations at scheduled intervals rather than continuously. Machine learning models are trained periodically on updated data, and defensive strategies are evaluated through periodic simulations, balancing the need for effective analysis with acceptable processing speeds and resource utilization.

Inventive Principle:

Principle #19Periodic action

Data Source

PatentUS11792229B2AI-driven defensive cybersecurity strategy analysis and recommendation system

Publication Date: 2023.10.17 QOMPLX INC

AI summary

A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators.