Dynamic ML Model Selector for Cybersecurity Log Analysis
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Solution Overview
Problem
Current cybersecurity threat monitoring systems rely heavily on manual analysis by human analysts, which is time-consuming and inefficient due to the overwhelming volume of data and high false positive rates, making it impractical for large enterprises to accurately detect anomalies in computer system logs.
Innovation Solution
Implementing a dynamic machine learning model selection mechanism that classifies incoming logs into specific subsets based on performance factors, allowing different machine learning models to process distinct subsets of security data effectively, thereby achieving higher predictive accuracy and reducing manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple machine learning models are trained and evaluated on each data instance to improve prediction accuracy, then the predictive accuracy improves, but the processing time and computational resources increase significantly
Solution Approach 1:
The system pre-trains multiple machine learning models on historical security data before deployment. During runtime, these pre-trained models are rapidly evaluated on incoming data instances without requiring retraining, thus maintaining high predictive accuracy while minimizing processing time. The models are prepared in advance to enable quick inference.
Solution Approach 2:
The system creates multiple copies of machine learning models with different architectures and training configurations. These model copies are deployed simultaneously and evaluated in parallel on the same data instances, allowing the system to leverage diverse model perspectives for improved accuracy without sequentially processing each model.
2Device complexity
If a single machine learning model is used to process all security data to simplify the system, then the device complexity is reduced, but the predictive accuracy decreases due to varying data characteristics
Solution Approach 1:
The system segments the security data processing task by dividing incoming data instances into different subsets based on their characteristics (e.g., log type, threat category, data source). Each segment is then processed by a specialized machine learning model trained on that specific data type, improving predictive accuracy while keeping individual model complexities manageable.
Solution Approach 2:
The system applies the principle of local quality by assigning different model evaluation criteria and parameters to different data subsets. Each model is optimized for its specific data domain with tailored hyperparameters and evaluation metrics, rather than using a uniform approach across all security data types.
3Measurement precision
If multiple machine learning models are evaluated on each data instance to improve accuracy, then the prediction quality improves, but the computational resources required increase
Solution Approach 1:
The system evaluates multiple machine learning models on each data instance, but not all models are fully executed for every input. Instead, the system uses preliminary filtering or confidence thresholding to determine which models need full evaluation, applying partial computation where sufficient and excessive computation only when necessary to achieve the desired prediction quality.
Data Source
AI summary
A machine learning model selector is provided. A set of machine learning (ML) models are trained based on a first training dataset. The set of trained ML model is executed on a second training dataset to generate a corresponding output for a set of data instances in the second training dataset. For each data instance in the set of data instances, a corresponding ranking of ML models is generated based on the corresponding output for the data instance generated by the set of ML models. A ML model selector is trained based on the data instances in the set of data instances and the corresponding ranking of ML models, to select a trained ML model based on an input data instance.