Bot Detection via JavaScript Profile Anomaly Scoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing bot detection techniques rely on hand-crafted rule-based methods, which are inadequate due to the high diversity and dynamic nature of web browsers and devices, limiting their applicability and requiring frequent updates, and lack automation in using client-side JavaScript or mobile app profiles.
Innovation Solution
An automated anomaly detection system using unsupervised machine learning and script code or mobile app profiles to identify bots by generating scores based on HTTP header and JavaScript parameters, combining multiple anomaly detection models, and incorporating hand-crafted rules for mitigation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand-crafted rule-based methods are used for bot detection, then specific inconsistencies can be detected, but the system requires frequent updates and significant human intervention due to high diversity and dynamicity of browsers and devices
Solution Approach 1:
The patent replaces manual hand-crafted rule-based detection with an automated machine learning system. The system uses unsupervised anomaly detection models that automatically analyze JavaScript profiles and HTTP headers to identify bots, eliminating the need for continuous manual rule creation and updates while maintaining detection accuracy across diverse browsers and devices
Solution Approach 2:
The system enables self-service bot detection by automatically learning from JavaScript profile data without requiring human intervention. The anomaly detection models continuously adapt to new browser and device configurations by processing profile information autonomously, allowing the system to serve itself by detecting and adapting to emerging patterns without manual reconfiguration
2Measurement precision
If hand-crafted rule-based methods are used for bot detection, then specific inconsistencies can be detected, but the system lacks scalability and applicability due to high diversity of web browsers, applications, and device configurations
Solution Approach 1:
The patent implements a universal bot detection system that handles multiple browser types, applications, and device configurations through a single anomaly detection framework. The system processes JavaScript profiles from various sources (browsers, mobile apps) using the same machine learning models, making it universally applicable across diverse platforms without requiring separate rules for each browser or device type
Solution Approach 2:
The system adapts to new browsers and devices by dynamically analyzing changes in JavaScript profile parameters. Instead of requiring pre-defined rules for each new browser version, the anomaly detection models automatically detect deviations in profile parameters (such as JavaScript engine versions, available APIs, and runtime characteristics) to identify bots masquerading as legitimate users across evolving platforms
3Reliability
If hand-crafted rule-based methods are used for bot detection, then detection can be performed, but maintaining the rule set involves significant human data analysis and experimentation
Solution Approach 1:
The patent replaces complex manual rule maintenance with automated machine learning models. The system uses unsupervised anomaly detection algorithms that automatically learn patterns from JavaScript profile data, eliminating the need for human data analysis and experimentation while improving detection reliability. The models continuously adapt to new patterns without requiring manual intervention, reducing system complexity despite handling diverse browser and device configurations
Data Source
AI summary
A system and method for detecting bots. The method includes receiving a request to access a server, the request is being received from a client device, and responsive to the request, causing the client device to download a script code file to the client device. The script code file, when executed, collects a profile, and the profile includes a plurality of parameters. The method also includes receiving the created profile, generating a score based on the plurality of parameters to identify a bot, and initiating a mitigation action based on the identified bot.