Stalkerware Classification Using Package and Marketplace Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods for detecting privacy-invasive applications, such as stalkerware, fail to distinguish between different types of malware, leading to ambiguous user notifications and inability to discern the intent of detected applications.
Innovation Solution
Utilizing multiple analysis techniques, including inspection of application package data and marketplace data, combined with program and marketplace machine learning classifiers, to predict sensitive data leaks and surveillance activities, and assign specific privacy invasion categories to applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional analysis techniques are used to detect privacy-invasive applications, then detection coverage is achieved, but the ability to distinguish between different types of malware is lost
Solution Approach 1:
The patent divides malware detection into multiple specialized analysis techniques, each targeting specific malware types. Static analysis detects stalkerware through code inspection, dynamic analysis identifies spyware through runtime behavior, and heuristic analysis catches unknown variants. This segmentation allows precise classification of different malware types rather than treating all privacy-invasive applications uniformly.
Solution Approach 2:
Different analysis techniques are applied to different aspects of malware detection based on their specific strengths. Static analysis is applied to code structure for stalkerware detection, dynamic analysis to runtime behavior for spyware identification, and heuristic analysis to suspicious patterns for unknown malware. Each technique operates with specialized quality tailored to its target malware type.
2Measurement precision
If multiple analysis techniques are implemented to distinguish malware types, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The complex detection system is segmented into three independent analysis modules: static analysis, dynamic analysis, and heuristic analysis. Each module operates autonomously with its own algorithms and data structures, managing complexity through modular design while maintaining high classification accuracy through specialized detection capabilities.
Solution Approach 2:
The patent merges multiple analysis techniques into a unified detection framework that combines their results. The static analysis results from code inspection, dynamic analysis from runtime monitoring, and heuristic analysis from pattern matching are integrated to produce comprehensive malware classification, achieving high accuracy while managing complexity through systematic integration.
3Loss of information
If traditional detection methods are used, then processing speed is maintained, but the ability to provide specific malware warnings is lost
Solution Approach 1:
Static analysis is performed preliminarily by examining application code and data structures before execution, identifying stalkerware characteristics such as unauthorized data access patterns and hidden communication protocols. This preliminary detection provides early warnings about malware intent without requiring full runtime execution, reducing overall analysis time while maintaining detection accuracy.
Solution Approach 2:
The system skips unnecessary analysis steps by using heuristic analysis to quickly identify and flag suspicious patterns in application behavior. When heuristic rules detect known malware signatures or suspicious patterns, the system can rapidly classify the application without requiring exhaustive static or dynamic analysis, thereby reducing analysis time while maintaining detection effectiveness.
Data Source
AI summary
The disclosed computer-implemented method for utilizing multiple analysis techniques to distinguish stalkerware from privacy-invasive application types may include (i) inspecting application package data associated with a target application, (ii) analyzing application marketplace data for the target application to determine descriptions of potential privacy-invasive actions, (iii) predicting, utilizing a program machine learning classifier, sensitive data leaks associated with the target application based on the application package data, (iv) predicting, utilizing a marketplace machine learning classifier, surveillance activities associated with the target application based on the descriptions of the potential privacy-invasive actions, and (v) performing a security action that assigns a privacy invasion category to the target application to distinguish the target application from stalkerware and other privacy invasion application types. Various other methods, systems, and computer-readable media are also disclosed.