Machine-Learning Resource Output Analysis for Threat Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing information handling systems are vulnerable to security threats and misuse, with challenges in analyzing computer use and managing resource output effectively.
Innovation Solution
Implementing machine learning techniques to analyze resource output, such as application displays, to detect malicious actions and unauthorized activities, without requiring access to traditional logs or APIs, thereby simplifying policy generation and enforcement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional log analysis and API monitoring are used to detect security threats, then detection capability is improved, but system complexity and administrative burden increase
Solution Approach 1:
The patent extracts and analyzes only the essential output elements (display content, window titles, icons) from the application interface rather than monitoring entire system logs or API calls. This selective extraction maintains security detection capability while reducing system complexity by focusing only on visible user interactions.
Solution Approach 2:
The patent introduces an intermediary analysis layer that captures application output through the display interface rather than directly accessing system logs or APIs. This intermediary approach simplifies the monitoring system by observing only what is presented to the user, reducing complexity while maintaining detection effectiveness.
2Measurement precision
If comprehensive resource monitoring is implemented to detect malicious activities, then security detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent segments the application output into distinct analytical elements (display content, window titles, icons, controls) rather than analyzing the entire output as a single unit. This segmentation enables parallel processing of different elements, improving detection accuracy while reducing overall processing time through divided computational tasks.
Solution Approach 2:
The patent applies partial action by analyzing only the most security-relevant portions of application output (such as window titles, file paths, and specific control elements) rather than processing every pixel or element. This selective analysis maintains high detection accuracy while significantly reducing computational overhead and processing time.
3Reliability
If detailed policy enforcement rules are implemented to control resource usage, then security control effectiveness is improved, but ease of operation and administrative simplicity decrease
Solution Approach 1:
The patent enables self-service policy enforcement by automatically analyzing application output elements and determining policy violations without requiring manual configuration of detailed rules. The system autonomously evaluates display content against security policies, reducing administrative burden while maintaining effective security control through automated decision-making.
Solution Approach 2:
The patent changes the parameter of policy enforcement from static, pre-configured rules to dynamic, context-aware evaluation based on actual application output. By monitoring real-time display elements and adapting policy checks to the current application state, the system achieves effective security control with simpler administrative oversight, as policies are enforced based on observed behavior rather than complex rule sets.
Data Source
AI summary
Methods and systems for machine learning analysis of resource output are disclosed that include acquiring a resource output (where the resource output is an output produced by a computing resource), generating a representation of the resource output (where the representation is generated by the machine learning system, and the machine learning system generates the representation based, at least in part, on the resource output), and, in response to an analysis of the representation against a representational statement, performing an operation (where the representational statement is in a representational language).