Prudent Ensemble Models for Network Security Blind Spot Detection
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Solution Overview
Problem
Existing machine learning models in network security face challenges in achieving high precision without retraining, particularly due to blind spots where models lack sufficient or conflicting evidence, leading to false predictions that are difficult to detect, especially when probability thresholds are high.
Innovation Solution
The implementation of prudent ensemble models that identify and mark blind spots, filtering out unreliable predictions by measuring prediction reliability and only using reliable predictions, thereby increasing precision and counteracting adversarial attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If probability threshold is increased to boost precision, then precision is improved, but false predictions still occur and model reliability decreases in blind spots
Solution Approach 1:
The patent segments the feature space into reliable regions and blind spots by analyzing coverage across multiple ensemble trees. Each tree's decision paths are examined to identify regions with sufficient evidence (covered by multiple trees) versus blind spots (sparsely covered or conflicting). This segmentation allows the system to treat different regions differently, maintaining high precision while identifying unreliable predictions.
Solution Approach 2:
The patent performs preliminary identification and marking of blind spots during the model training phase, before actual predictions are made. By pre-characterizing the feature space and marking blind spots in advance, the system can quickly identify unreliable predictions during inference without sacrificing precision. This preliminary action enables the system to know which regions to trust and which to flag for further review.
2Reliability
If ensemble model is used to improve prediction reliability, then model robustness is improved, but blind spots far from decision boundary cannot be detected
Solution Approach 1:
The patent moves blind spot detection from the traditional decision boundary dimension to a new dimension of feature space coverage analysis. Instead of only examining regions near decision boundaries, the system analyzes how many ensemble trees cover each region and the consistency of their predictions. This dimensional shift enables detection of blind spots anywhere in the feature space, including those far from decision boundaries, by measuring evidence sufficiency rather than proximity to boundaries.
Solution Approach 2:
The patent implements a feedback mechanism where the ensemble model's own prediction structure is analyzed to identify its limitations. By examining which regions are sparsely covered or have conflicting predictions across trees, the system generates feedback about its own blind spots. This self-feedback enables the model to identify and flag its own unreliable predictions, improving detection capability without external intervention.
3Measurement precision
If further processing is performed on all uncertain predictions, then precision is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies local quality by treating different prediction regions differently based on their reliability characteristics. Predictions in well-covered regions with consistent ensemble agreement are processed quickly with standard procedures. Predictions in blind spots or regions with conflicting evidence are flagged for more thorough processing. This localized differentiation ensures high precision for critical cases while maintaining fast processing for reliable predictions, optimizing the time-precision tradeoff.
Data Source
AI summary
Systems and methods include receiving a content item between a user device and a location on the Internet or an enterprise network; utilizing a trained machine learning ensemble model to determine whether the content item is malicious; responsive to the trained machine learning ensemble model determining the content item is malicious or determining the content item is benign but such determining is in a blind spot of the trained ensemble model, performing further processing on the content item; and, responsive to the trained machine learning ensemble model determining the content item is benign with such determination not in a blind spot of the trained machine learning ensemble model, allowing the content item. A blind spot is a location where the trained machine learning ensemble model has not seen any examples with a combination of features at the location or has examples with conflicting labels.