Normalized Request Sequences for Real-Time Misappropriation Detection
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Solution Overview
Problem
Current security methods are inadequate in identifying and preventing misappropriation of data access in real-time.
Innovation Solution
A system utilizing an AI machine learning engine to analyze normalized request sequences to identify and prevent misappropriation by distinguishing between legitimate users and malicious actors through pattern recognition and escalation actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current security methods are used to monitor data access, then some level of security is maintained, but real-time identification and prevention of misappropriation is not achieved
Solution Approach 1:
The system transforms security monitoring from static rule-based checks to dynamic behavioral analysis by changing the parameter of detection from discrete access events to continuous request sequence patterns. This enables real-time identification of misappropriation while maintaining processing speed through normalized sequence comparison.
Solution Approach 2:
The system performs preliminary normalization of request sequences and pre-establishes training sets of legitimate and malicious patterns before actual security evaluation. This preliminary preparation enables rapid real-time comparison and decision-making without compromising processing speed during actual security events.
2Measurement precision
If complex analysis methods are employed to distinguish legitimate users from malicious actors, then detection accuracy improves, but processing speed may be impaired
Solution Approach 1:
The system changes the detection parameter from analyzing individual request characteristics to comparing normalized request sequences against pre-established training sets. This parameter transformation maintains high detection accuracy through pattern recognition while preserving processing speed by avoiding complex real-time analysis of individual requests.
Solution Approach 2:
The system creates simplified copies of request sequences in normalized form for comparison purposes. By working with normalized sequence representations rather than full complex request details, the system achieves accurate detection while maintaining processing efficiency through reduced computational complexity.
Data Source
AI summary
Systems, computer program products, and methods are described herein for identifying and preventing misappropriation using normalized request sequences. The method includes receiving a sequence of two or more requests associated with a user. The method also includes comparing the sequence of two or more requests with one or more past sequence of two or more past requests. The method further includes determining whether the sequence of two or more requests were carried out by the user or the malfeasant actor. The determination is made based on a comparison of the sequence of two or more requests with at least one of the one or more past sequence of two or more past requests. The method also includes causing an escalation action to be executed in an instance in which the sequence of two or more requests was carried out by the malfeasant actor.