Deception Detection Module Using User Interaction Signatures
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Solution Overview
Problem
Computer systems are vulnerable to deceptive actions, such as anti-virus scams, where users are tricked into performing suspicious activities that compromise their security and data integrity, with existing technologies failing to effectively detect such scams in real-time.
Innovation Solution
A computer-implemented method and system that detects user interaction activities, logs them, and compares them against predefined signatures to determine if they match suspicious patterns, generating notifications to alert users of potential deception, utilizing a deception identification module that includes detection, comparison, and alert generation modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user interaction activities are continuously monitored and logged to detect deceptive actions, then the reliability of deception detection is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The system segments the monitoring function into distinct modules: a detection module that identifies user interaction activities, a logging module that stores activity records, and a comparison module that matches activities against signatures. This segmentation allows each module to operate independently and optimally, improving reliability while managing complexity through modular architecture.
Solution Approach 2:
The system performs preliminary actions by pre-defining deception signatures and patterns before actual deception detection is needed. The comparison module is initialized with a database of known deceptive activity patterns, enabling rapid matching against current user interactions without requiring complex real-time analysis, thus reducing processing complexity while maintaining high detection reliability.
2Speed
If real-time detection of user interaction activities is implemented, then the speed of deception identification is improved, but the use of energy and processing resources increases
Solution Approach 1:
Instead of continuously analyzing every user interaction in detail, the system creates simplified representations (signatures) of known deceptive patterns. The comparison module then matches current activities against these pre-created signatures using straightforward pattern recognition algorithms, achieving rapid detection without the energy-intensive complex analysis that would be required for comprehensive real-time monitoring of all user behaviors.
Solution Approach 2:
The system applies partial action by monitoring only the most critical user interaction activities that are most indicative of deception, rather than analyzing all possible user behaviors. The detection module focuses on specific actions such as file downloads, remote access attempts, and data access patterns, which are the most likely to indicate deceptive activity, thereby reducing processing energy consumption while maintaining effective detection speed for the most important security threats.
3Measurement precision
If user interaction activities are logged and analyzed to identify patterns, then the measurement precision of deceptive actions is improved, but the loss of time for data processing and analysis increases
Solution Approach 1:
The system performs preliminary action by pre-processing and storing deception signatures in a readily accessible database before actual detection is needed. When user interaction activities are logged, the comparison module can immediately match them against the pre-prepared signatures using efficient pattern matching algorithms, achieving high measurement precision while minimizing real-time processing time since the heavy lifting of pattern definition has already been completed in advance.
Solution Approach 2:
The system extracts only the essential features and key characteristics from user interaction activities for logging and analysis, rather than storing and processing complete activity records. By focusing on the most indicative actions and their patterns, the system achieves precise deception identification while reducing the volume of data that requires processing, thereby decreasing time loss associated with data analysis.
Data Source
AI summary
A computer-implemented method to determine whether user interaction activities are indicative of deceptive actions is described. Performance of at least one user interaction activity on a computing device is detected. The performance of the at least one user interaction activity is logged. A determination is made as to whether the logged user interaction activity matches a signature. A notification message is generated based on the determination that the logged user interaction activity matches the signature.