Behavioral Metrics for Non-Human User Detection
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Solution Overview
Problem
Conventional authentication mechanisms and anti-virus/malware detection software have limitations in detecting malicious non-human users, such as bots, on computing devices, leading to security threats due to security weaknesses and human errors, especially when users do not employ sufficient anti-virus/malware detection software.
Innovation Solution
The system captures raw data from user interactions and analyzes behavioral metrics like mouse activity, keyboard interactions, and sensor data to distinguish between human and non-human users without relying on CAPTCHAs, using processing logic that can be implemented in hardware, software, or firmware to identify and flag non-human user behavior patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional authentication mechanisms and anti-virus/malware detection software are used, then basic security protection is provided, but malicious non-human users (bots) can still masquerade as human users and exploit security weaknesses
Solution Approach 1:
The patent changes the detection parameters from static authentication credentials to dynamic behavioral metrics. By monitoring mouse activity patterns, keyboard interaction timing, sensor data, and navigation behavior, the system detects subtle deviations that indicate bot presence. This parameter transformation enables reliable detection of non-human users while maintaining compatibility with existing authentication mechanisms.
Solution Approach 2:
The patent replaces traditional mechanical authentication systems (passwords, CAPTCHAs) with a sensor-based behavioral analysis system. By substituting manual authentication with automated tracking of user interactions through sensors and processing logic, the system achieves superior adaptability against bots without compromising security reliability.
2Adaptability or versatility
If CAPTCHA methods are used to distinguish human users from bots, then bot detection capability is improved, but user experience deteriorates and implementation complexity increases
Solution Approach 1:
The system performs bot detection automatically in the background without requiring user intervention. Processing logic continuously analyzes behavioral metrics from sensors and user interactions, making detection decisions without presenting CAPTCHAs or requiring user actions. This self-service approach maintains high bot detection capability while eliminating the complexity and user experience issues associated with traditional CAPTCHA methods.
Solution Approach 2:
The patent introduces processing logic as an intermediary between user interactions and security decisions. This intermediary component analyzes behavioral metrics and determines bot presence without requiring direct user engagement with security mechanisms. The intermediary approach simplifies implementation by centralizing detection logic while maintaining adaptability against various bot types.
3Ease of operation
If users do not employ sufficient anti-virus/malware detection software, then device accessibility is maintained, but security vulnerabilities increase significantly
Solution Approach 1:
The patent merges bot detection functionality with the core authentication and user interaction processing systems. By combining behavioral analysis with existing security infrastructure, the system provides enhanced protection without requiring separate software installations or complicating device accessibility. This merging approach eliminates security vulnerabilities while maintaining ease of operation.
Data Source
AI summary
Methods and systems for malicious non-human user detection on computing devices are described. The method includes collecting, by a processing device, raw data corresponding to a user action, converting, by the processing device, the raw data to features, wherein the features represent characteristics of a human user or a malicious code acting as if it were the human user, and comparing, by the processing device, at least one of the features against a corresponding portion of a characteristic model to differentiate the human user from the malicious code acting as if it were the human user.