Fraud Detection Visualizer for Cyber-Attack Behavioral Analysis
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Solution Overview
Problem
Current systems lack an efficient tool to visualize and analyze behavioral patterns of users, making it difficult for organizations to differentiate between legitimate and fraudulent users, and to effectively respond to cyber-attacks, as they cannot store or replay behavioral components of attacks and do not provide real-time analysis of cyber-attacks.
Innovation Solution
A system comprising a visualization module that monitors user interactions, extracts unique features, and compares them to stored profiles to detect fraudulent activities, using a Fraud Detection Module to trigger mitigating actions, and a fraud investigator visualizer console to provide graphical representations of fraud scores and allow for real-time analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual review of user interactions is used to detect fraud, then detection accuracy can be maintained, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The system creates visual copies of user interaction sequences as graphical timelines, allowing investigators to review fraud cases visually without manual analysis of raw data. The visualization module generates graphical representations of user interactions that can be quickly scanned and compared, maintaining detection accuracy while reducing review time.
Solution Approach 2:
The patent replaces manual mechanical review processes with automated computational systems. The fraud detection module automatically analyzes user interaction sequences, calculates fraud scores, and generates visualizations, substituting human manual analysis with automated algorithms that process data faster while maintaining or improving detection accuracy.
2Reliability
If detailed behavioral analysis is performed on all user interactions, then fraud detection capability is improved, but system complexity and computational resources increase
Solution Approach 1:
The system extracts only the most relevant behavioral features from user interactions, such as typing patterns, mouse movements, and sequence timing. The fraud detection module focuses on specific extracted features rather than analyzing all raw interaction data, reducing system complexity while maintaining reliable fraud detection capability.
Solution Approach 2:
The patent segments the fraud detection system into distinct functional modules: interaction capture, feature extraction, fraud scoring, and visualization. This modular segmentation allows each component to handle specific tasks independently, reducing overall system complexity while enabling comprehensive behavioral analysis for reliable fraud detection.
3Speed
If real-time analysis of user interactions is implemented, then response time to cyber-attacks is reduced, but computational load and processing requirements increase
Solution Approach 1:
The system performs partial analysis by focusing on key behavioral indicators and critical interaction sequences rather than processing every single user action in real-time. The fraud detection module calculates fraud scores based on selected features and patterns, reducing computational load while maintaining fast response time to detect and alert on cyber-attacks.
Solution Approach 2:
The patent implements rapid processing of user interactions by skipping detailed analysis of normal, low-risk behaviors and focusing computational resources on suspicious or anomalous patterns. The system rushes through routine interactions using pre-established baselines and only performs intensive analysis when fraud indicators are detected, reducing overall computational load while maintaining real-time response capability.
Data Source
AI summary
Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. An end-user device (a desktop computer, a laptop computer, a smartphone, a tablet, or the like) interacts and communicates with a server of a computerized server (a banking website, an electronic commerce website, or the like). The interactions are monitored, tracked and logged. User Interface (UI) interferences are intentionally introduced to the communication session; and the server tracks the response or the reaction of the end-user to such communication interferences. The system determines whether the user is a legitimate human user; or a cyber-attacker posing as the legitimate human user. The system displays gauges indicating cyber fraud scores or cyber-attack threat-levels. The system extrapolates from observed fraud incidents and utilizes a rules engine to automatically search for similar fraud events and to automatically detect fraud events or cyber-attackers.