Machine Learning Model Evaluation for Cyber Defense
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Solution Overview
Problem
Current machine learning model evaluation methods for cyber defense fail to effectively detect obfuscated cyber attacks in real-time network traffic, as they do not account for obfuscated attacks and rely on static data sets, leading to inadequate model performance and human subjective retraining decisions.
Innovation Solution
A process that involves obtaining and classifying network traffic samples, splitting them into training and test sets, obfuscating cyber attack samples, and using these obfuscated samples in an enhanced test set to evaluate intrusion detection capability, along with real-time anomaly detection and model fit analysis to determine optimal model parameters and trigger retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis is used for machine learning model evaluation, then model fit can be determined for typical uses, but the evaluation fails to detect obfuscated cyber attacks in real-time network traffic
Solution Approach 1:
The patent applies preliminary action by pre-obfuscating training data samples before model training. The system creates obfuscated versions of known attack patterns in advance, incorporating them into the training set so the machine learning model learns to recognize obfuscated attack patterns before deployment. This preparatory obfuscation enables the model to detect obfuscated attacks in real-time without requiring complex evaluation adjustments later.
2Ease of manufacture
If machine learning models are trained on static data sets, then model training is simplified, but the models cannot effectively evaluate real-time, evolving network traffic
Solution Approach 1:
The patent implements dynamics by enabling continuous model retraining with newly obfuscated samples derived from real-time network traffic. The system maintains a feedback loop where detected attacks are obfuscated and added to the training set, allowing the model to adapt to evolving attack patterns while preserving the simplicity of the original training pipeline. This dynamic updating ensures the model remains reliable against new obfuscation techniques without complicating the training process.
3Device complexity
If conventional model evaluation methods are used, then evaluation process is simple, but retraining decisions are made subjectively without objective criteria
Solution Approach 1:
The patent applies feedback by implementing an automated evaluation system that measures model performance on obfuscated test samples and uses these objective metrics to trigger retraining decisions. The system calculates performance degradation thresholds and automatically initiates retraining when obfuscated attack detection accuracy falls below these thresholds, eliminating subjective human judgment while maintaining a relatively simple evaluation process through standardized metric comparison.
Data Source
AI summary
Testing machine learning sensors by adding obfuscated training data to test data, and performing real time model fit analysis on live network traffic to determine whether to retrain.