Bot Detection via Input Anomaly Injection and Behavioral Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current security systems fail to effectively distinguish between human users and automated cyber-attacks or malware that mimic human interactions, leading to false positives and negatives in detecting fraudulent activities on electronic devices.
Innovation Solution
A system that monitors user interactions and injects input/output anomalies to differentiate between human and automated inputs, using contextual analysis and machine learning algorithms to classify user behavior and generate a fraud risk score, thereby identifying and mitigating potential cyber threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated detection systems are used to identify cyber-attacks, then detection speed and coverage are improved, but false positives and false negatives increase due to inability to distinguish human from automated behavior
Solution Approach 1:
The system dynamically adapts detection methods based on real-time analysis of input patterns. It transitions between passive monitoring and active challenge modes, adjusting the level of intervention based on detected anomalies. This dynamic approach allows the system to maintain high detection speed while improving accuracy by applying more sophisticated analysis only when necessary.
Solution Approach 2:
The system introduces an intermediary challenge mechanism that acts as a mediator between the detection system and the user. When suspicious patterns are detected, the system inserts challenges (such as CAPTCHA-like tests or behavioral verification) that automatically differentiate human users from bots. This intermediary layer resolves the contradiction by providing automated detection capability while maintaining high accuracy through the challenge-response mechanism.
2Device complexity
If traditional security measures are implemented, then system simplicity is maintained, but false positive and false negative rates remain high
Solution Approach 1:
The security system is segmented into multiple independent modules: passive monitoring module, anomaly detection module, challenge generation module, and verification module. Each module performs a specific function with well-defined interfaces. This segmentation allows the system to achieve high reliability through modular design while keeping individual components relatively simple, resolving the contradiction between complexity and reliability.
Solution Approach 2:
The system employs self-service mechanisms where the detection system automatically analyzes input patterns, identifies anomalies, generates appropriate challenges, and verifies responses without human intervention. The system self-adjusts its detection thresholds and challenge difficulty based on accumulated data. This automation improves reliability while maintaining operational simplicity, as the system manages its own complexity internally.
3Ease of operation
If passive monitoring of user interactions is used, then user experience is maintained, but detection accuracy is insufficient to differentiate human from automated inputs
Solution Approach 1:
The system prepares anti-bot measures in advance by continuously learning normal human behavior patterns during passive monitoring. When automated behavior is detected, pre-prepared challenges are immediately deployed. This preliminary preparation allows the system to maintain good user experience during normal operation while having accurate detection capabilities ready when needed, resolving the contradiction between ease of operation and detection precision.
Data Source
AI summary
Devices, systems, and methods of detecting whether an electronic device or computerized device or computer, is being controlled by a legitimate human user, or by an automated cyber-attack unit or malware or automatic script. The system monitors interactions performed via one or more input units of the electronic device. The system searches for abnormal input-user interactions; or for an abnormal discrepancy between: the input-unit gestures that were actually registered by the input unit, and the content that the electronic device reports as allegedly entered via such input units. A discrepancy or abnormality indicates that more-possibly, or necessarily or certainly, a malware or automated script is controlling the electronic device, rather than a legitimate human user. Optionally, an input-output aberration or interference is injected, in order to check for manual corrective actions that only a human user, and not an automated script, is able to perform.