Bot Detection Service Integration with CDN Edge Server
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Solution Overview
Problem
Existing systems face challenges in effectively differentiating between human and bot interactions in distributed computer networks, particularly with sophisticated botnets that hide behind proxies or have minimal anomalies in their header signatures, leading to false negatives and security breaches.
Innovation Solution
An overlay network edge server interacts with a bot detection service by setting cookies and injecting a data collection script to gather sensor data from clients, which is then evaluated to determine if the client is human or a bot, using a threat score-based system to decide whether to forward requests to the origin server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional header signature analysis is used to differentiate bots from humans, then the detection process is simple and fast, but sophisticated botnets hiding behind proxies can evade detection causing false negatives
Solution Approach 1:
The detection system is divided into multiple independent components: edge server for initial filtering, bot detection service for comprehensive analysis, and JavaScript-based fingerprinting for behavioral analysis. Each component handles specific aspects of bot detection, allowing the system to achieve high accuracy without requiring any single component to be overly complex.
Solution Approach 2:
A bot detection service acts as an intermediary between the CDN edge server and the origin server. This intermediary service receives requests, performs sophisticated bot detection using multiple techniques including JavaScript execution and sensor data collection, and then decides whether to allow requests to pass through to the origin server.
2Measurement precision
If comprehensive sensor data collection is implemented to accurately identify bots, then detection accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary bot detection at the CDN edge server using basic header analysis and IP reputation checks before requests reach the bot detection service. This preliminary filtering eliminates many obvious bot requests early, reducing the workload and processing time for more comprehensive detection methods.
Solution Approach 2:
Sensor data collection occurs periodically through JavaScript-executed events rather than continuously. The system collects behavioral data at specific interaction points (clicks, scrolls, form submissions) which provides sufficient information for accurate classification without requiring constant monitoring that would consume excessive processing time.
3Reliability
If JavaScript-based fingerprinting is deployed to collect behavioral data, then the ability to detect sophisticated bots improves, but the integration complexity and deployment effort increase
Solution Approach 1:
The JavaScript-based detection technology is designed to be self-configuring and automatically deploys across the CDN network. The system automatically receives configuration parameters from the customer portal, generates appropriate JavaScript code, and distributes it to edge servers without requiring manual intervention for each deployment scenario.
Solution Approach 2:
The bot detection service provides multiple detection methods through a single unified platform: header analysis, IP reputation checking, JavaScript-based behavioral analysis, and sensor data collection. This multi-functional approach allows the system to adapt to different detection needs without requiring separate systems for each technique.
Data Source
AI summary
A server interacts with a bot detection service to provide bot detection as a requesting client interacts with the server. In an asynchronous mode, the server injects into a page a data collection script configured to record interactions at the requesting client, to collect sensor data about the interactions, and to send the collected sensor data to the server. After the client receives the page, the sensor data is collected and forwarded to the server through a series of posts. The server forwards the posts to the detection service. During this data collection, the server also may receive a request from the client for a protected endpoint. When this occurs, and in a synchronous mode, the server issues a query to the detection service to obtain a threat score based in part on the collected sensor data that has been received and forwarded by the server. Based on the threat score returned, the server then determines whether the request for the endpoint should be forwarded onward for handling.