Browser Agent Detection via Active Probing Code Snippets
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Solution Overview
Problem
Current methods for detecting automated traffic on web pages are inadequate as they rely on passive metrics that can be matched by both human and bot behavior, leading to ineffective differentiation between human and automated interactions, especially in the context of advertising fraud.
Innovation Solution
An active probing model is implemented that collects qualitative metrics by inserting code snippets into web pages to monitor user interactions, utilizing JavaScript and Flash to differentiate between human and automated activity by analyzing elements such as mouse events, timing, and interface behavior, and transmitting data to analysis servers for real-time reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If passive detection systems are used to monitor network traffic, then the system can be implemented on the network without interference and data can be recovered, but the detection quality is low because legitimate users may have unusual access patterns, caching layers prevent requests like automated bots, and bots are increasingly matching passive metrics
Solution Approach 1:
The patent introduces an intermediary analysis server that acts as a mediator between the web server and browsers. This server receives requests, inserts tracking code snippets into pages, and analyzes returned data to detect bot behavior. The intermediary approach enables active probing without directly interfering with network traffic flow, resolving the contradiction between detection accuracy and system complexity.
Solution Approach 2:
The patent replaces passive network traffic analysis with active JavaScript-based probing mechanisms. Instead of mechanically analyzing network packets, the system uses JavaScript code snippets embedded in web pages to actively query and collect data about the browser environment, enabling more accurate bot detection while maintaining system simplicity.
2Measurement precision
If active probing with code snippets is implemented to collect qualitative metrics, then detection accuracy improves by exposing behavioral discrepancies, but the complexity of the system increases due to code insertion and data transmission mechanisms
Solution Approach 1:
The patent extracts the detection logic into separate JavaScript code snippets that are inserted into web pages and executed in the browser environment. This extraction allows the complex measurement and analysis functions to be separated from the core system, enabling high-precision behavioral analysis while keeping the main system structure relatively simple.
Solution Approach 2:
The analysis server performs multiple functions: it inserts code snippets into pages, receives and processes returned data, analyzes behavioral patterns, and generates detection results. This multi-functionality consolidates complex operations into a single universal system component, reducing overall system structural complexity while maintaining high measurement precision.
3Ease of manufacture
If passive detection methods are used, then the implementation is simpler with less system modification, but the detection effectiveness is reduced because bots can match human-like passive metrics while legitimate users may exhibit unusual patterns
Solution Approach 1:
The patent implements preliminary action by pre-inserting JavaScript code snippets into web pages before they reach users. These code snippets are prepared in advance to actively probe browser characteristics and collect behavioral data, enabling reliable bot detection from the outset rather than requiring complex post-processing of passive metrics.
Data Source
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AI summary
A method for determining if a web browser is being operated by a human or a non-human agent, based on analysis of certain aspects of how a user interacts with a webpage. By placing a code snippet into the code of a webpage prior to a given user accessing thai webpage, one is able to evaluate the user's actions in order to predict the type of user. The predictions are made by acquiring information on how the user loads, navigates, and interacts with the webpage and comparing that information with statistics taken from a control group. Performance metrics from all webpages containing similar code elements are compiled by analysis servers and made a vailable to the operator of a webpage through a variety of reporting mediums. By compiling such performance metrics, the method helps combat and prevent malicious automated traffic directed at advertisements and other aspects of a given webpage.