Automated Bot Blocking Through Confidence-Based Scraping Detection
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Solution Overview
Problem
Existing systems are inefficient in distinguishing between good and bad bots, leading to unauthorized scraping of digital resources, and current methods like CAPTCHA tests and IP blocklisting are either ineffective or costly to maintain.
Innovation Solution
A system that collects and analyzes multiple data points from client interactions, compares them against pre-defined criteria, and determines a confidence score to classify interactions as authorized or unauthorized, using a bad bot detection model to limit access to digital resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods like CAPTCHA tests and IP blocklisting are used to block bots, then access control is provided, but these methods are either ineffective against sophisticated bots or costly to maintain
Solution Approach 1:
The system changes the parameters of bot detection by transitioning from simple binary classification (block/allow) to a multi-dimensional confidence scoring system that evaluates multiple interaction parameters simultaneously, including cursor movement patterns, click behavior, form filling speed, and navigation sequences
Solution Approach 2:
The system introduces an intermediary confidence score between the detection process and the blocking decision, allowing for nuanced responses such as challenges or monitoring instead of immediate blocking, thereby improving both accuracy and user experience
2Measurement precision
If multiple data points are collected and analyzed to improve bot detection accuracy, then unauthorized scraping is reduced, but the system complexity and processing requirements increase
Solution Approach 1:
The system segments the bot detection process into multiple independent analysis modules, each evaluating specific interaction aspects (cursor movement, clicking, form filling, navigation), allowing for targeted optimization and reduced overall complexity
Solution Approach 2:
The system performs preliminary analysis of interaction data points as they are collected, continuously updating confidence scores in real-time, which allows for early detection and reduced processing burden compared to analyzing all data points after collection
3Reliability
If real-time analysis of client interactions is performed to detect bad bots, then unauthorized access is prevented, but processing time and computational resources increase
Solution Approach 1:
The system performs continuous but lightweight analysis of interaction streams, maintaining running confidence scores without interrupting the user experience, allowing for real-time detection with minimal processing overhead
Solution Approach 2:
The system applies partial analysis to all interactions (basic confidence score updates) and excessive/detailed analysis only when confidence scores indicate suspicious behavior, optimizing the balance between detection reliability and processing time
Data Source
AI summary
A system for limiting access to a digital resource based on detection of unauthorized scraping of the digital resource includes one or more processors configured to execute the instructions to receive a request from a client device to access a digital resource, determine at least a first attribute and a second attribute associated with the request, determine a first confidence score based in part on evaluating the first attribute relative to the second attribute, detect interaction data representing interactions by the client device with the digital resource, determine a second confidence score based at least in part on the interaction data, the second confidence score indicating that the interaction data is unauthorized, detect unauthorized scraping of the digital resource based at least in part on the second confidence score, and limit access of the client device to the digital resource.