Network Traffic Profiling for App Identification
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Solution Overview
Problem
Existing methods struggle to accurately determine app usage statistics on mobile devices without accessing internal device operations, limiting market insights and scalability.
Innovation Solution
A system using a proxy server with a client record, traffic monitoring, and profiling components applies machine learning to analyze network traffic patterns to identify installed apps and generate usage statistics, separating client traffic into positive and negative training sets to determine app-specific patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods are used to determine app usage statistics, then device internal access is required, but accuracy and scalability are limited
Solution Approach 1:
The patent introduces a proxy server as an intermediary component that mediates between the network traffic and the analysis system. The proxy server captures, logs, and forwards network traffic packets containing application identifiers, enabling external observation of app usage without requiring direct access to device internals. This intermediary approach resolves the contradiction by providing accurate measurement through indirect observation.
2Adaptability or versatility
If network traffic analysis is used to identify applications, then device internal access is avoided, but measurement precision may be reduced
Solution Approach 1:
The system performs preliminary actions by having the proxy server capture and log network traffic packets in advance, storing them in a structured format with application identifiers extracted and preserved. This preliminary capture and organization of data enables subsequent machine learning analysis to achieve high precision without requiring real-time device access, thus resolving the contradiction between scalability and measurement precision.
Solution Approach 2:
The patent replaces the mechanical approach of direct device access with an information-based approach using network traffic analysis. By substituting physical access mechanisms with network packet inspection and machine learning algorithms, the system achieves both scalability across multiple devices and maintained measurement precision through intelligent pattern recognition.
3Measurement precision
If machine learning is applied to analyze network traffic patterns, then analysis accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the network traffic analysis into distinct components: packet capture by proxy server, extraction of application identifiers, organization into training datasets, and separate machine learning model training for each application. This segmentation reduces overall system complexity by dividing the complex task into manageable, independent modules that can be processed separately and combined for high-precision results.
Data Source
AI summary
Techniques to identify applications based on network traffic are described. In one embodiment, an apparatus may comprise a client record component, a traffic monitoring component, a profiling component, and a traffic analysis component. The client record component may be operative to store a client application map, the client application map to represent installations of a plurality of applications on a plurality of client devices. The traffic monitoring component may be operative to monitor training network traffic and additional network traffic on one or more network interfaces, the training network traffic generated by the plurality of client devices. The profiling component may be operative to generate a network profile map using machine learning based on the training network traffic and the client application map. The traffic analysis component may be operative to identify one or more application of the plurality of applications. Other embodiments are described and claimed.