Specific File Detection in ML Pipelines for Inline Malware Analysis
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Solution Overview
Problem
Existing malware detection systems face challenges in efficiently identifying and mitigating newly created malware due to resource constraints and the need for real-time analysis, especially in environments where signature-based methods are insufficient.
Innovation Solution
Implementing machine learning models on data appliances for inline malware detection, utilizing features like n-grams and classification models to analyze files in real-time, enabling rapid identification of malicious files without relying solely on signature matching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If signature-based malware detection methods are used, then detection reliability for known malware is improved, but detection speed and real-time analysis capability deteriorate due to resource constraints and verification delays
Solution Approach 1:
The system pre-computes and stores malware detection models during offline training phases, preparing classification models, n-gram features, and decision trees in advance. This preliminary action enables the appliance to perform rapid inline detection without performing heavy computation during real-time operation, thus resolving the contradiction between detection reliability and real-time analysis speed.
2Measurement precision
If comprehensive malware analysis is performed, then detection precision is improved, but processing time and resource consumption increase
Solution Approach 1:
The malware detection process is segmented into offline model training phase and online detection phase. The offline phase performs comprehensive feature extraction, model training, and validation to ensure high precision. The online phase uses pre-trained models for rapid classification. This segmentation allows comprehensive analysis without real-time performance degradation.
Solution Approach 2:
The system transforms complex malware characteristics into simplified n-gram features and statistical parameters that can be rapidly processed. By changing the representation parameters from raw binary data to extracted features like n-gram frequencies and file metadata, the system achieves both high precision and fast processing speed.
3Productivity
If machine learning models are implemented on data appliances, then real-time malware detection capability is improved, but device complexity and resource requirements increase
Solution Approach 1:
The patent replaces complex mechanical computation systems with optimized machine learning models that use simpler mathematical operations. By substituting traditional signature matching and heuristic analysis with pre-trained classification models that perform rapid scoring and classification, the system reduces computational complexity while maintaining or improving detection capability.
Data Source
AI summary
A set of features including a first feature and a second feature is received at a server. A subset of the set of features is determined for use in generating a model usable by a device to locally make a malware classification decision. The device has reduced computing resources as compared to computing resources of the server. The subset of the set of features is used to generate the model. The generated model includes the first feature and does not include the second feature. A determination is made, at a time subsequent to the generation of the model, that an updated model should be deployed to the device. An updated model is generated.