Cyber Security Restoration Engine Simulating Network Assets

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Solution Overview

Problem

Existing cybersecurity approaches struggle to effectively train organizations for cyber incidents and maintain up-to-date playbooks due to the rapidly evolving threat landscape, leading to inefficiencies in response and remediation.

Innovation Solution

The development of an AI-based cybersecurity system that utilizes simulated cyber security scenarios to train users and generate bespoke playbooks tailored to specific incident circumstances and network infrastructures.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If conventional cybersecurity products are used to detect and prioritize cyber threats, then threat detection capability is improved, but the ability to train users and maintain up-to-date playbooks deteriorates due to the rapidly evolving threat landscape

Engineering Contradiction:

Improvethreat detection capabilityVSAvoiduser training and playbook maintenance

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system performs preliminary actions by generating simulated cyber attack scenarios and using them to train users before real attacks occur. The simulation engine creates hypothetical attack sequences and uses reinforcement learning to train digital twins of security operations center agents, enabling proactive preparation rather than reactive response.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates digital twins - virtual copies of security operations center agents and attack scenarios. These digital twins replicate the behavior and decision-making processes of human operators, allowing training and experimentation in a virtual environment without risking real systems. The simulated scenarios are copies of potential real-world attacks used for preparation.

Inventive Principle:

Principle #26Copying

2Reliability

If simulated cyber security scenarios are generated and used for training, then user readiness and adaptive response strategies are improved, but system complexity increases due to AI-based simulation engine

Engineering Contradiction:

Improveuser readinessVSAvoidsimulation engine complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The simulation engine uses reinforcement learning to automatically generate and refine attack scenarios without requiring manual configuration by security experts. The digital twins self-train through iterative simulation cycles, automatically adjusting their strategies based on outcomes. This automation reduces the operational burden despite the underlying system complexity.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system dynamically adjusts parameters of the simulation environment, such as attack complexity, defense strategies, and scenario variables, to optimize training effectiveness. By changing parameters rather than redesigning the entire system, the engine adapts to different training needs while managing complexity through configurable adjustments.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS20250030725A1Cyber security scenarios with simulated incidents

Publication Date: 2025.01.23 DARKTRACE HLDG LTD

AI summary

An apparatus comprises a cyber security restoration engine configured to simulate an asset of a computing network that is involved in a simulated cyberattack. The cyber security restoration engine is configured to generate data representative of a simulated cyber security scenario involving the asset of the computing network. The simulated cyber security scenario is derived from a real world cyber security scenario mapped to the asset.