Threat Response System Using Trained Model Simulation
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Solution Overview
Problem
Current systems for threat response in computer environments often fail to detect and mitigate cyber threats efficiently, leading to delayed remediation and increased damage due to the need for manual investigation and response, which can compromise business continuity and data privacy.
Innovation Solution
A system that includes processors communicatively coupled to a computer environment, capable of detecting threats, identifying affected assets, and simulating predefined resolutions using trained models to select and implement an appropriate response, such as quarantining assets, re-routing traffic, or modifying security rules, based on real-time traffic and processing loads.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual investigation and response methods are used, then system complexity is reduced, but response time increases and damage expands
Solution Approach 1:
The system pre-defines multiple resolution actions (quarantine, block, terminate, etc.) and their corresponding parameters before threats are detected. When a threat is identified, the system can immediately apply pre-configured resolutions without requiring manual investigation, thereby reducing response time while maintaining manageable system complexity through automation.
Solution Approach 2:
The threat response system automatically detects threats, evaluates them against predefined criteria, selects appropriate resolutions, and implements responses without human intervention. This self-service capability eliminates manual investigation delays while the predefined resolution framework keeps system complexity controlled through structured automation.
2Productivity
If automated threat response is implemented, then response speed improves, but system complexity increases
Solution Approach 1:
The automated response system is segmented into distinct functional modules: threat detection, evaluation, resolution selection, and implementation. Each module handles specific tasks independently, which improves overall response speed while keeping individual component complexities manageable. The segmentation allows the system to process threats efficiently without requiring overly complex monolithic architecture.
Solution Approach 2:
The system uses predefined parameters and thresholds for threat evaluation and resolution selection. By changing operational parameters (such as threat severity levels, response thresholds, and resolution criteria) rather than restructuring the entire system, the automated response can adapt to different threat scenarios efficiently, maintaining high response speed without proportionally increasing system complexity.
3Reliability
If predefined resolutions are simulated before implementation, then response accuracy improves, but processing time increases
Solution Approach 1:
The system performs simulation of predefined resolutions but limits the scope to critical evaluation points rather than exhaustive testing of all possible outcomes. This partial simulation approach provides sufficient accuracy for threat response decisions while avoiding the excessive processing time that would result from complete simulation of all resolution scenarios.
Solution Approach 2:
The system pre-simulates and evaluates resolution outcomes during the definition phase, storing the results for rapid retrieval during actual threat responses. This preliminary evaluation creates a knowledge base that improves response accuracy without requiring time-consuming simulations during active threat incidents, thereby reducing processing time while maintaining reliability.
Data Source
AI summary
Systems and methods for threat response in computer environments can include detecting, by one or more processors, a threat to the computer environment, and identifying a subset of assets of the plurality of assets associated with the threat. The one or more processors can determine from a predefined set of resolutions a plurality of resolutions executable to resolve the threat for the subset of assets. The one or more processors can execute, for each resolution of the plurality of resolutions, a trained model to simulate the resolution for the subset of assets. The one or more processors can select, based at least on results of execution of each resolution, a resolution among the plurality of resolutions to be implemented.