Device-Level Security Scanning for Unauthorized Components
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Solution Overview
Problem
Existing systems lack an effective method to continuously monitor and remediate security vulnerabilities in computing devices due to hardware and software changes, leading to potential unauthorized use or data leakage.
Innovation Solution
A system that performs continuous device-level scanning and monitoring using machine learning to generate metadata lists for hardware and software components, analyzes new components for security risks, and initiates remediation processes such as isolation or deletion if necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous device-level scanning and monitoring is implemented, then security vulnerability detection capability is improved, but system resource consumption increases
Solution Approach 1:
The system performs preliminary actions by generating comprehensive metadata lists of hardware and software components before security incidents occur. These baseline metadata lists enable rapid comparison and detection of unauthorized changes without requiring continuous deep scanning of all components, thus improving security detection while reducing ongoing resource consumption.
Solution Approach 2:
The system creates copies of hardware and software metadata into structured lists that can be efficiently monitored and compared. Instead of continuously analyzing original complex system states, the system works with simplified metadata copies that capture essential security-relevant information, reducing computational overhead while maintaining detection effectiveness.
2Measurement precision
If deep scanning of hardware and software elements is performed, then security monitoring accuracy is improved, but scanning time increases
Solution Approach 1:
The system extracts only the essential security-relevant metadata from hardware and software elements into structured lists, rather than performing complete deep scans of all system components. This extraction approach maintains monitoring accuracy by capturing critical security attributes while significantly reducing the time required for scanning and analysis.
Solution Approach 2:
The system changes the parameters of monitoring by transitioning from comprehensive deep scanning of all system attributes to focused metadata-based monitoring of specific security-critical parameters. This parameter transformation enables accurate security detection with reduced scanning time by concentrating resources on the most relevant security indicators.
3Extent of automation
If machine learning-based continuous scanning is implemented, then automated security analysis is improved, but processing complexity increases
Solution Approach 1:
The system segments the complex security analysis task into distinct components: metadata collection, metadata list generation, new component detection, and security assessment. By dividing the automation process into these manageable segments, the system achieves high levels of automated security analysis while keeping individual processing modules relatively simple and maintainable.
Data Source
AI summary
A system is provided for remediation of security vulnerabilities in computing devices using continuous device-level scanning and monitoring. In particular, the system may perform a deep scan of the hardware and software elements of a computing device and/or application and compile the information from the deep scan into a hardware metadata list and a software metadata list associated with the computing device and/or application. The system may then, through a machine learning-based process, continuously scan the elements within the hardware metadata list and the software metadata list to identify the elements that are not involved in the operation of the computing device and/or application. The system may flag such elements for inspection to evaluate the safety of the elements and subsequently execute one or more remediation processes in response to detecting an unsafe element.