Automated Threat Mitigation via Classification Model
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Solution Overview
Problem
Existing computing systems face challenges in efficiently addressing unknown threat scenarios due to the lack of established remediation protocols, leading to inefficient and costly manual processes for IT specialists, who often struggle to identify and implement appropriate mitigation strategies.
Innovation Solution
A computing system generates a mitigation file with predictive remediation processes by analyzing threat vectors and applying a classification model to identify relevant processes from a network of client systems, creating a composite remediation file that includes both non-executable and executable instructions for immediate action.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring and updating of antivirus software is performed by IT specialists, then threat mitigation capability is maintained, but cost and time consumption increase significantly
Solution Approach 1:
The system enables self-service by allowing the computing system to automatically detect unknown threat scenarios and generate its own mitigation processes without requiring human intervention. The classification model autonomously analyzes threat vectors and produces tailored mitigation strategies, freeing IT specialists from manual monitoring tasks while maintaining reliable threat mitigation capability.
Solution Approach 2:
The patent replaces the mechanical manual process of IT specialists monitoring and updating antivirus software with an automated electronic system. The classification model and mitigation generation process substitute human expertise with algorithmic analysis, dramatically reducing time consumption while preserving threat mitigation effectiveness through systematic evaluation of threat scenarios.
2Adaptability or versatility
If IT specialists manually search for remediation protocols online, then response to unknown threats is attempted, but efficiency decreases and best remediation processes cannot be identified
Solution Approach 1:
The system replaces the inefficient manual online search process with an automated classification model that systematically analyzes threat scenarios. The model evaluates threat vectors against known patterns and generates optimized mitigation processes, dramatically improving productivity while maintaining adaptability to unknown threats through continuous learning and pattern recognition.
Solution Approach 2:
The system incorporates feedback mechanisms where the classification model continuously learns from analyzed threat scenarios and refines its mitigation generation capabilities. This feedback loop enables the system to improve its response efficiency to unknown threats over time, adapting to new threat patterns while maintaining high productivity through automated decision-making.
3Reliability
If comprehensive monitoring software is deployed to detect all threats, then threat detection capability improves, but system complexity and cost increase
Solution Approach 1:
The patent extracts the essential threat detection and mitigation functions from complex comprehensive monitoring software. By isolating the core classification model and mitigation generation processes, the system achieves reliable threat detection capability while reducing overall system complexity. The extracted functions operate independently as a focused solution rather than part of a庞大的 monitoring infrastructure.
Data Source
AI summary
A computing system performs real-time mitigations for unfamiliar threat scenarios by identifying a particular threat scenario for a client system that has not previously experienced the threat scenario and for which a remediation process is unknown. The computing system responds to the unknown threat scenario by generating and providing the client system a mitigation file that includes a predictive set of mitigation processes for responding to the threat scenario. The mitigation file is generated by first generating a threat vector that identifies a plurality of different threat scenario characteristics for the particular threat scenario. Then, a classification model is applied to the threat vector to identify a predictive set of mitigation processes that are determined to be a best fit for the threat vector and that are included in the mitigation file.