Sparse-Data Real-Time Cyber Detection with Gradient-Boosted Trees
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Solution Overview
Problem
The practical implementation of artificial intelligence for real-time cyber incident detection in data sparse environments is hindered by the need for large amounts of high-quality training data, specialized knowledge, and the challenge of producing accurate and precise results with minimal latency.
Innovation Solution
The use of a novel artificial intelligence architecture that includes a gradient boosted decision tree trained in data sparse environments, a data transformation step to minimize latency and ensure constant lookup time, and an output that generates a confidence score for detecting fraudulent and unauthorized communications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional artificial intelligence models are used for fraud detection, then real-time determination capability is achieved, but the models fail to perform accurately in data sparse environments
Solution Approach 1:
The patent changes the fundamental parameters of the AI model by switching from neural networks to gradient boosted decision trees, which are inherently more suitable for data sparse environments. This parameter change allows the model to achieve high detection accuracy without requiring large amounts of training data, directly resolving the contradiction between reliability and data quantity.
Solution Approach 2:
The patent employs a simpler, more efficient model architecture (gradient boosted decision trees) that can be quickly trained and deployed without requiring extensive computational resources or large datasets. This approach treats the modeling process as a lightweight operation that can be adapted to small datasets, effectively addressing the data sparsity issue.
2Speed
If complex data transformation is applied to ensure constant lookup time, then real-time processing is achieved, but system complexity increases
Solution Approach 1:
The patent segments the data transformation process into distinct stages: data cleaning, feature extraction, and model input preparation. By breaking down the complex transformation into manageable segments, the system achieves constant lookup time while keeping each segment's complexity low and manageable.
Solution Approach 2:
The patent performs data transformation and feature extraction in advance during model training, creating a pre-processed feature set that can be quickly queried during real-time fraud detection. This preliminary action eliminates the need for complex transformations during actual detection, achieving fast lookup times without increasing operational complexity.
3Measurement precision
If high precision detection is required to avoid false positives, then detection accuracy is improved, but latency increases
Solution Approach 1:
The patent replaces complex neural network computations with simpler gradient boosted decision tree algorithms that are optimized for speed and precision in data sparse environments. This substitution of the computational mechanism enables high-precision detection while maintaining low latency, as the decision tree model can make accurate determinations much faster than traditional neural networks.
Solution Approach 2:
The patent changes the model parameters and architecture to prioritize both precision and speed. By using gradient boosted decision trees with optimized hyperparameters, the system achieves high detection precision without the latency penalty that would normally accompany such accuracy requirements.
Data Source
AI summary
The systems and methods use a gradient boosted decision tree, which may be trained in data sparse environments. The system also uses a data transformation step to collapse complex data into a standardized feature input (e.g., a fixed length feature input) that may be processed by the model with a constant (or near-constant) lookup time and with minimal latency. Finally, the system generates a dual variable output that provides both a metric of whether a communication is fraudulent and/or unauthorized as well as a confidence level of that determination.