Bot Detection Confidence Scoring with Adaptive Multi-Algorithm Signals
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Solution Overview
Problem
Existing bot detection systems struggle to accurately differentiate between human and automated attacks due to evolving bot behaviors and vulnerabilities in telemetry-based detection methods, leading to inconsistent performance across different customer environments.
Innovation Solution
A bot detection system that scores requests as a probability of being a bot, using parallel detection algorithms and real-time statistical models for autotuning, incorporating all threat detections and adapting to customer traffic trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If telemetry-based detection algorithms are used to identify bots, then detection capability is provided, but the system becomes vulnerable to improved synthetic telemetry from evolving bots
Solution Approach 1:
The patent combines multiple detection algorithms (telemetry-based, configuration-based, behavioral analysis) into a unified scoring system that evaluates requests from multiple angles simultaneously, making it harder for bots to evade detection by improving only one aspect of their synthetic telemetry
Solution Approach 2:
The system dynamically adapts detection thresholds and scoring weights based on learned bot patterns and traffic characteristics, allowing it to respond to evolving bot behaviors rather than relying on static detection rules that bots can eventually bypass
2Productivity
If detection algorithms are applied in order of precedence, then processing efficiency is improved, but flexibility to accommodate different customer environments and traffic distributions is reduced
Solution Approach 1:
The detection system is segmented into independent scoring components that can be individually configured and weighted, allowing each detection algorithm to operate independently while contributing to the overall score, thus maintaining processing efficiency while enabling customization
Solution Approach 2:
The system allows dynamic adjustment of scoring parameters, weights, and thresholds based on customer-specific traffic patterns and environmental factors, enabling the same core detection engine to adapt to different customer environments without sacrificing processing speed
3Device complexity
If fixed detection thresholds are used, then system simplicity is maintained, but accuracy in distinguishing bots from humans across varying traffic conditions deteriorates
Solution Approach 1:
The system incorporates feedback loops where detection results and traffic patterns are continuously analyzed to automatically adjust thresholds and scoring parameters, maintaining high accuracy without requiring complex manual configuration or sacrificing system simplicity
Data Source
AI summary
A bot detection service associated with an overlay network operates to score traffic as a probability of being a bot, as opposed to returning a binary classification (i.e., bot or human). According to the approach herein, scoring is determined through probability estimates, wherein a score (the probability) is based on considering a set of detections concurrently. In one embodiment, all (or substantially all) triggered (current) threat detections contribute to the score. The preferred approach penalizes requests that fail all (or substantially all) combinations of detection algorithms. According to a further feature, an automated tuning (autotuning) is also applied, e.g., using real-time empirical statistical models, to adapt the measurement of false positive probability for one or more threat detection algorithms to suit customer traffic trends. The approach herein is also extensible to include any number of future threat detection algorithms.