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 malfeasant actors, including the capability to train and update a training set based on past request patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current security methods are used to monitor data access, then some level of security monitoring is achieved, but real-time identification and prevention of misappropriation cannot be accomplished
Solution Approach 1:
The system transforms raw request data into normalized request sequences, changing the parameter representation from individual requests to sequential patterns. This normalization enables more effective comparison and identification of misappropriation behaviors while maintaining real-time processing capability through standardized data structures that can be efficiently analyzed
Solution Approach 2:
The system performs preliminary analysis by comparing current request sequences against a database of known misappropriation patterns before actual misappropriation occurs. By pre-establishing security baselines and continuously comparing incoming requests against these patterns, the system can identify and prevent misappropriation in real-time rather than reacting after damage occurs
2Measurement precision
If comprehensive security analysis is performed on all requests, then misappropriation detection accuracy improves, but processing speed deteriorates
Solution Approach 1:
The system extracts only the essential sequential patterns from request data that are indicative of misappropriation behavior. By focusing on normalized request sequences rather than analyzing every detail of each individual request, the system achieves high detection accuracy for misappropriation while maintaining fast processing speeds for legitimate requests that follow normal patterns
Solution Approach 2:
The system applies full security analysis selectively - performing comprehensive comparison against misappropriation patterns only when request sequences show suspicious characteristics, while allowing requests that clearly match established legitimate user patterns to pass through with minimal processing. This partial application of rigorous analysis maintains both accuracy and speed
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.