Mobile App Security Framework Using Region-Based Code Analysis
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Solution Overview
Problem
The security of mobile application software on devices such as smartphones and tablets is a growing concern due to the increasing capability and importance of applications handling sensitive information, with existing technologies failing to efficiently assess and ensure the safety of these applications.
Innovation Solution
A framework that includes a static instrumentation engine, dynamic runtime test environment, central intelligence engine, platform-specific knowledge base, machine learning platform, and user-provided rules to identify and analyze 'regions of interest' in application code, applying stimuli and monitoring behaviors to determine if an application is safe or unsafe, utilizing virtual machines and monitoring functions to streamline testing and prevent malicious actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive security testing is performed on mobile applications, then security coverage is improved, but testing time and computational resources are increased
Solution Approach 1:
The patent segments the application code into basic blocks and identifies critical regions of interest, focusing security testing on these specific segments rather than performing comprehensive testing on the entire codebase. This segmentation allows thorough analysis of critical areas while avoiding unnecessary testing of non-critical code, thus improving security coverage efficiently without excessive time consumption.
Solution Approach 2:
The patent applies local quality by concentrating testing resources on specific regions of interest within the application code that are more likely to contain security vulnerabilities. By identifying and focusing on critical code blocks rather than uniform testing across all code, the system achieves better security coverage in high-risk areas while reducing overall testing time.
2Reliability
If comprehensive security testing is performed on mobile applications, then security coverage is improved, but computational resources are increased
Solution Approach 1:
The patent divides the application into basic blocks and identifies critical regions of interest, concentrating computational resources on analyzing only these segmented critical areas rather than performing exhaustive analysis on the entire application. This segmentation strategy maintains high security coverage while significantly reducing the computational resources and energy required for testing.
Solution Approach 2:
The patent applies partial action by performing security testing on only the critical regions of interest rather than the entire application codebase. This partial testing approach achieves sufficient security coverage for the most vulnerable areas while avoiding the excessive computational resource consumption that would result from comprehensive full-code testing.
3Ease of manufacture
If traditional security scanning is used, then implementation is simple, but security coverage is insufficient
Solution Approach 1:
The patent introduces an intermediary layer between simple scanning and comprehensive analysis by using a region identification module that automatically identifies critical regions of interest. This intermediary component enables the system to achieve improved security coverage through focused analysis while maintaining implementation simplicity, as the region identification is automated and does not require complex manual configuration.
Data Source
AI summary
A method is described that includes receiving an application and generating a representation of the application that describes specific states of the application and specific state transitions of the application. The method further includes identifying a region of interest of the application based on rules and observations of the application's execution. The method further includes determining specific stimuli that will cause one or more state transitions within the application to reach the region of interest. The method further includes enabling one or more monitors within the application's run time environment and applying the stimuli. The method further includes generating monitoring information from the one or more monitors. The method further includes applying rules to the monitoring information to determine a next set of stimuli to be applied to the application in pursuit of determining whether the region of interest corresponds to improperly behaving code.