Web Bot Detection via Multi-Tiered Session Analysis
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Solution Overview
Problem
Conventional Bot detection systems are not robust enough and often inadvertently block human traffic, returning false positives, as they rely solely on IP addresses to differentiate between human and Bot traffic, leading to performance degradation of Web servers.
Innovation Solution
A multi-tiered detection and differentiation architecture that includes a Bot confidence module for initial analysis and a human confidence module to analyze session traits, distinguishing human behavior from Bot activity, even when originating from the same IP address.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If IP address-based Bot detection is used, then Bot traffic can be identified and blocked, but human traffic may be inadvertently blocked (false positives)
Solution Approach 1:
The detection system is divided into multiple independent modules: IP address analysis module, session trait analysis module, and confidence scoring module. Each module analyzes different aspects of traffic independently and combines results to make final determination, reducing false positives by not relying solely on IP address
Solution Approach 2:
The system transitions from single-dimension IP address-based detection to multi-dimensional detection by incorporating session traits (number of webpages visited, time spent, navigation patterns). This additional dimensional analysis enables more accurate differentiation between human and Bot traffic
2Measurement precision
If multi-tiered detection architecture is implemented, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The complex detection task is segmented into manageable modules: initial Bot confidence module for quick filtering, then human confidence module for detailed analysis of borderline cases. This modular segmentation reduces overall system complexity while maintaining high accuracy
Solution Approach 2:
The Bot confidence module performs preliminary analysis and filtering before traffic reaches the more complex human confidence module. This preliminary action eliminates obvious Bot traffic early, reducing the burden on subsequent complex analysis stages
3Productivity
If Bot traffic is blocked aggressively, then server performance degradation is prevented, but customer experience may be negatively impacted
Solution Approach 1:
The system continuously monitors traffic patterns and adjusts detection thresholds based on observed behavior. Legitimate human traffic patterns are learned and fed back into the detection algorithm, allowing the system to distinguish between actual Bot traffic and legitimate customer traffic, maintaining both server performance and customer experience
Data Source
AI summary
Web Bot detection methods and systems are provided that receive a request, in connection with a network session. The methods and systems determine whether the request is associated with potential Bot activity, and based thereon assign a Bot confidence designation. The Bot confidence designation indicates a likelihood that the request represents an agent-based request. The methods and systems analyze a session trait of the network session relative to predetermined session traits indicative of human-based requests, and assign a human confidence designation based on the analysis. The human confidence designation indicates a likelihood that the request represents a human-based request. The request is then classified to represent an agent-based request or human-based request based on the Bot and human confidence designations.