ML Access-Right Validation Using Interaction Log Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to accurately determine whether access-right requests are invalid due to a lack of tracked or unavailable data on user interactions, leading to ineffective post-processing evaluations and missed detection of fraudulent or unauthorized activities.
Innovation Solution
A primary load management system collects feature sets from user interactions, trains a machine-learning model to identify patterns, and generates predictions on access-right requests to determine their validity, using a central repository for log lines and supervised learning algorithms to classify invalid actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If third-party systems evaluate previously assigned access rights after the resource is no longer available, then invalid actions can be detected, but the system interactions that led to the previous assignment are not collected, disparately stored, or effectively managed
Solution Approach 1:
The system collects and stores system interaction data before the resource becomes unavailable, rather than attempting to evaluate historical data after the fact. The primary load management system continuously captures feature sets characterizing user interactions with the system, preserving this data in a centralized location for future analysis by machine learning models.
Solution Approach 2:
A centralized data storage mechanism acts as an intermediary between the distributed system interactions and the evaluation process. The system stores log lines representing user interactions in a unified location, enabling effective retrieval and analysis without relying on disparate distributed storage across multiple systems.
2Device complexity
If data representing user interactions is not tracked or is unavailable, then system complexity is reduced, but determining whether a new access right request is invalid becomes a technical challenge
Solution Approach 1:
The primary load management system performs multiple functions: it manages access rights assignment, collects user interaction data, stores log lines, and provides this data to machine learning models for invalid action detection. This multi-functional approach consolidates what would otherwise require separate specialized systems.
Solution Approach 2:
The system automatically collects and stores its own operational data without requiring external intervention. The primary load management system captures feature sets from user interactions and stores them as log lines, making this data self-available for validation purposes without needing separate data collection infrastructure.
3Measurement precision
If machine learning models are trained on collected feature sets, then predictions of invalid access right requests improve, but data processing time and computational resources increase
Solution Approach 1:
The system continuously collects and stores feature sets characterizing user interactions as preliminary data preparation, so that when new access right requests need validation, the machine learning model already has pre-processed training data available, reducing real-time processing requirements.
Data Source
AI summary
The present disclosure generally relates to a primary load management system configured to execute machine learning and artificial intelligence techniques to generate predictions of access-right requests that are or are likely to be invalid before the access-right requests are processed for assignment to users or user devices. The present disclosure relates to systems and methods that collect a data set representing characteristics of user devices as the user devices interact with various systems of the primary load management system and train a machine-learning model to predict invalid access-right requests using the collected data set. The collected data set may include a log line that represents each user device, and each log line may be labeled based on an invalidity evaluation. New access-right requests can be processed using the trained machine-learning model to determine whether or not to assign access rights in response to the access-right request.