Automated DPI Rule Generation for Mobile App Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting mobile application usage on devices via network traffic are inefficient and lack automated processes for tracking usage data, especially for newly released applications.
Innovation Solution
A VPN application with a deep packet inspection (DPI) module that monitors network traffic, uses a rules dictionary with regular expressions to identify and track mobile application usage, and automatically generates rules for new applications by downloading and launching them to determine their network traffic patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to detect mobile application usage via network traffic, then implementation is simpler, but efficiency and accuracy are insufficient especially for newly released applications
Solution Approach 1:
The system performs preliminary actions by automatically downloading and launching new applications before actual usage detection begins. This allows the DPI module to capture initial network traffic patterns and generate detection rules in advance, improving efficiency for newly released applications without requiring complex manual configuration
Solution Approach 2:
The detection system serves itself by automatically generating detection rules through automated application execution and network traffic analysis. The DPI module autonomously creates and updates detection patterns without external intervention, maintaining high efficiency while managing complexity through self-configuration
2Measurement precision
If automated processes are implemented to track usage data for all applications including new ones, then tracking accuracy improves, but system complexity increases
Solution Approach 1:
The system implements feedback loops where the DPI module continuously monitors network traffic, compares it against stored detection rules, and automatically updates rules based on observed traffic patterns. This feedback mechanism ensures high detection accuracy while managing complexity through iterative self-improvement rather than complex static rule sets
Solution Approach 2:
The system creates simplified copies of application behavior patterns through detection rules that represent typical network traffic characteristics. Instead of complex real-time analysis of every packet, the system uses copied behavioral patterns from automated observation to accurately identify application usage with simpler detection logic
3Measurement precision
If deep packet inspection is used to monitor network traffic, then usage data accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The DPI module extracts only the essential and distinguishing features from network traffic packets that are sufficient for application identification. Rather than analyzing complete packet contents, the system extracts key behavioral patterns and characteristics, maintaining detection accuracy while significantly reducing processing time and computational overhead
Data Source
AI summary
Disclosed is a method for tracking usage of applications on a mobile device that includes a virtual private network (VPN) application. The VPN application includes a rule dictionary to store a rule for each application that is executable on the mobile device. A rule comprises an identifier for the application and an associated regular expression. To create a rule, a new application is installed on the mobile device and executed to create network traffic. The network traffic from the new application is inspected and a regular expression that matches the new application is created and stored in the rules dictionary. Once the rule is stored, a usage of the application can be identified based on monitoring the network traffic and searching the rule dictionary to identify a matching application. Once a matching application is found, the usage metric of the application is updated.