Sandbox Grayware Analysis for Detecting Unwanted App Behavior
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Solution Overview
Problem
Existing app stores lack effective methods to identify and mitigate grayware applications, which perform unwanted functions without causing harm to user devices, often disguising themselves as legitimate software and evading detection through lack of continuous monitoring and security checks.
Innovation Solution
A method and system for grayware analysis that involves running software applications in a sandbox environment, collecting activity information, matching it to grayware behavior descriptions, and performing corrective actions to mitigate identified grayware activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous monitoring and security checks are implemented, then grayware detection capability is improved, but system complexity and computational overhead increase
Solution Approach 1:
The system performs preliminary analysis of applications before they are installed on user devices. By analyzing applications in a sandbox environment prior to deployment, the system can identify grayware behaviors without requiring continuous monitoring during actual use, thereby improving detection capability while avoiding the complexity of ongoing surveillance systems
Solution Approach 2:
The patent introduces a sandbox environment as an intermediary between the application developer and the end user. This isolated environment allows security analysis to be performed without interfering with normal device operations, enabling reliable grayware detection while maintaining system simplicity for the end user
2Measurement precision
If sandbox environment is used for analysis, then grayware identification accuracy is improved, but analysis time and processing resources increase
Solution Approach 1:
The system performs analysis only on specific critical behaviors and activities within the sandbox environment rather than monitoring all possible operations. By focusing on particular grayware indicators and using selective monitoring, the system achieves accurate identification while reducing the time and resources required for comprehensive analysis
Solution Approach 2:
The sandbox environment enables continuous execution of the application under observation, allowing the system to capture grayware behaviors as they naturally occur during normal operation. This continuous observation approach improves identification accuracy by capturing genuine usage patterns rather than requiring artificial testing scenarios
3Reliability
If activity information collection is expanded, then detection of unwanted functions is improved, but data processing complexity increases
Solution Approach 1:
The system segments the collection and analysis of activity information into distinct categories and types. By organizing data into structured groups (e.g., file operations, network activity, registry changes), the system can comprehensively detect unwanted functions while simplifying the processing complexity through systematic data organization and targeted analysis of specific behavior patterns
Data Source
AI summary
Methods and systems for grayware analysis include running a software application in a sandbox. Activity information is collected from the software application that represents actions performed by the software application within an environment of the sandbox. The collected activity information is matched to a grayware activity description to identify the software application as performing a grayware activity. A corrective action is performed on the software application.