Synthetic Malicious Sample Generation for Malware Detection
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Solution Overview
Problem
Current machine learning-based malware detection systems face challenges in detecting new variants and modifications of malware due to the high cost and scarcity of labeled data, and the evolving nature of malware that adapts to evade detection.
Innovation Solution
A security device generates new synthetic malicious samples using a combination of statistical and formal grammar models, which are then refined with unlabeled real data to create realistic samples that can be used for training classifiers, enabling the detection of new and previously unseen malware threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning methods are used for malware detection, then detection capability is improved, but the cost and time for labeling training data increases
Solution Approach 1:
The patent creates synthetic copies of malware samples by generating artificial training data that mimics real malware characteristics. This copying approach allows the system to produce unlimited training samples without requiring manual labeling of actual malware, thus improving detection capability while avoiding the time-consuming labeling process.
Solution Approach 2:
The system employs unsupervised learning algorithms that automatically analyze and learn from unlabeled network traffic data without human intervention. The algorithm self-organizes to identify malware patterns, eliminating the need for manual labeling while maintaining high detection reliability.
2Measurement precision
If machine learning models are trained on available data, then detection performance is improved, but the ability to detect evolved malware variants deteriorates
Solution Approach 1:
The patent implements a dynamic training system where synthetic malware samples are continuously generated to reflect evolving malware variants. The system adapts by creating new training data that matches emerging threat patterns, allowing the detection model to maintain high accuracy against both known and new malware variants through ongoing retraining with updated synthetic data.
3Adaptability or versatility
If more labeled data is collected for training, then model generalization is improved, but the cost and resources required increase
Solution Approach 1:
Instead of collecting and labeling large volumes of real malware data, the system generates synthetic copies that replicate the statistical properties and behavioral characteristics of malware. This approach provides sufficient training data volume for improving model generalization without incurring the costs associated with data collection, storage, and manual labeling.
Data Source
AI summary
In one embodiment, a security device in a computer network determines a plurality of values for a plurality of features from samples of known malware, and computes one or more significant values out of the plurality of values, where each of the one or more significant values occurs across greater than a significance threshold of the samples. The security device may then determine feature values for samples of unlabeled traffic, and declares one or more particular samples of unlabeled traffic as synthetic malicious flow samples in response to all feature values for each synthetic malicious flow sample matching a respective one of the significant values for each corresponding respective feature. The security device may then use the samples of known malware and the synthetic malicious flow samples for model-based malware detection.