Bot Detection System Using Anomaly Pattern Segmentation
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Solution Overview
Problem
Existing bot detection techniques often produce false positives, incorrectly identifying human users as bots due to noise in data and algorithmic processing errors, leading to unnecessary flagging of legitimate human transactions.
Innovation Solution
A bot detection system with client-side data collection using Javascript code or SDKs for behavioral data and device fingerprinting, combined with server-side processing involving anomaly checks and a multi-stage filtering process to differentiate between human and bot transactions, utilizing statistical and machine learning techniques to reduce false positives by identifying patterns in anomaly data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If anomaly detection techniques are used to detect bots, then bot detection capability is improved, but false positive rate increases
Solution Approach 1:
The patent segments the anomaly detection process into multiple independent analysis components, each evaluating specific aspects of user behavior (mouse movements, keyboard typing patterns, scroll behavior, click patterns). By dividing the detection into separate modules that each handle specific behavioral dimensions, the system reduces false positives while maintaining comprehensive bot detection capability.
Solution Approach 2:
The patent introduces an intermediary analysis layer that processes raw anomaly data through multiple filtering stages before making final bot detection decisions. This intermediary process includes validating anomaly consistency across different behavioral metrics and cross-referencing against known human behavior patterns, thereby reducing false positives while preserving true bot detections.
2Measurement precision
If multiple data collection metrics are gathered to improve detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal data collection framework using JavaScript code that can gather multiple types of behavioral metrics (mouse movements, keyboard input, scroll behavior, click patterns) through a single integrated client-side script. This multi-functional approach consolidates what would otherwise require separate complex collection mechanisms into one unified system that handles all behavioral data gathering.
Solution Approach 2:
The client-side JavaScript code automatically collects behavioral data without requiring manual configuration or intervention. The system self-configures the data collection parameters and automatically processes the gathered information through the analysis pipeline, reducing the operational complexity of managing multiple data collection metrics.
Data Source
AI summary
This disclosure describes a bot detection system that distinguishes bot transactions from human transactions. The system utilizes an anomaly-based filter process to reduce the number of false positives as determined by the system. The filter process includes maintaining a database of anomaly patterns, wherein the patterns are encoded as anomaly pattern strings. As anomalies are detected, they are encoded in the anomaly pattern strings, and the database is updated by maintaining counts on the occurrences of the strings. When a particular pattern string as reflected in the database has a count that exceeds a threshold, the string is determined to be associated with a bot as opposed to a human user.