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 attack strategies due to their complexity, with current methodologies being largely reactive and unable to predict and counter new threats effectively.

Innovation Solution

A system and method for automated cybersecurity defensive strategy analysis that uses machine learning algorithms to simulate attack and defense strategies against a model of the networked system created using a directed graph, generating recommendations based on cost/benefit analysis.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If reactive cybersecurity methodologies are used to respond to discovered attacks, then response actions can be taken, but the system remains vulnerable to new attack strategies until patches are applied

Engineering Contradiction:

Improvecybersecurity defense reliabilityVSAvoidtime vulnerability exposure

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The system performs preliminary actions by using machine learning algorithms to predict and simulate future attack strategies before they are discovered in the wild. The defensive strategy simulator proactively tests potential attacks and evaluates defensive measures in advance, allowing organizations to implement defenses before actual attacks occur, thereby eliminating the time vulnerability exposure inherent in reactive approaches.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system implements continuous feedback loops where simulation results inform subsequent defensive strategy adjustments. The cost-benefit analysis engine uses feedback from simulated attack outcomes to dynamically adjust defensive priorities and resource allocation, enabling the system to adapt and improve defenses based on predicted threat evolution rather than waiting for actual attacks to be discovered and patched.

Inventive Principle:

Principle #23Feedback

2Reliability

If comprehensive cybersecurity defense strategies are implemented, then system security is improved, but the complexity of analyzing and managing defenses increases exponentially

Engineering Contradiction:

Improvecybersecurity defense reliabilityVSAvoiddefense analysis complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent introduces a directed graph model as an intermediary representation of the networked system, which simplifies the complex relationships between components. This graph-based abstraction enables the simulator to efficiently analyze defense strategies without dealing with the full exponential complexity of the actual system, making comprehensive security analysis tractable while maintaining accuracy.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system replaces manual, mechanical analysis of cybersecurity defenses with automated machine learning algorithms and simulation engines. The AI-driven predictive analytics and automated cost-benefit analysis eliminate the need for complex human analysis processes, substituting computational automation for manual defense strategy evaluation and management.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

3Loss of time

If AI-driven predictive analytics are implemented, then new attack strategies can be predicted proactively, but computational resources and analysis time are required

Engineering Contradiction:

Improvetime vulnerability exposureVSAvoidcomputational resource consumption

Core Design Contradiction:

Loss of timeVSUse of energy by moving object

Solution Approach 1:

The system applies partial action by focusing computational resources on predicting and analyzing only the most critical and likely attack vectors rather than exhaustively analyzing all possible attacks. The cost-benefit analysis engine prioritizes defensive strategies based on predicted impact and likelihood, allowing the system to achieve effective proactive defense with reduced computational overhead by concentrating resources on high-value targets.

Inventive Principle:

Principle #16Partial or excessive action

Data Source

PatentUS20250175455A1Ai-driven defensive cybersecurity strategy analysis and recommendation system

Publication Date: 2025.05.29 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.