Bitstream Feature Tensor Dataset for Malware Detection
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Solution Overview
Problem
Current methods for detecting malware in bitstreams, particularly non-executable files, are inefficient and often fail to identify malicious content within machine learning models, as they primarily focus on executable files and require extensive offline processing.
Innovation Solution
A method and apparatus for generating a dataset by applying transforms to bitstreams to produce feature tensors, labeling them, and using these tensors to train a content detection machine learning model, enabling the identification and classification of various types of content, including malware and corrupted files, within bitstreams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional pattern matching methods are used to detect malware in bitstreams, then detection accuracy for known malware can be maintained, but processing speed becomes too slow for real-time detection and non-executable files are not effectively detected
Solution Approach 1:
The patent replaces traditional mechanical pattern matching systems with machine learning models that process bitstreams. Instead of using deterministic pattern matching algorithms that compare bitstreams against known malware signatures, the system uses trained neural networks to automatically learn and detect malware patterns, enabling both real-time processing and detection of previously unseen malware variants including non-executable files.
Solution Approach 2:
The patent transforms the input data parameters by converting raw bitstreams into feature representations suitable for machine learning processing. This includes extracting relevant features from the bitstream data and transforming them into a format that can be effectively processed by the trained model, thereby improving both processing speed and detection capability.
2Measurement precision
If supervised learning is used to train malware detection models, then detection accuracy for known malware improves, but the models fail to detect new malware variants and non-executable files
Solution Approach 1:
The patent creates a universal detection model that can handle multiple types of files and malware variants through a single machine learning system. The model is designed to process various file types including executable and non-executable files, and can detect different malware categories (ransomware, viruses, worms, trojans, spyware, keyloggers) using the same underlying architecture, thereby achieving both accuracy and versatility.
Solution Approach 2:
The patent performs preliminary training of the machine learning model on diverse datasets that include both known malware and benign files. By pre-training the model with comprehensive data covering various file types and malware variants, the system gains the ability to generalize to new threats and non-executable files without requiring retraining, thus improving adaptability while maintaining detection accuracy.
3Measurement precision
If extensive offline processing is performed to build malware datasets, then detection capability can be improved, but the process becomes too time-consuming and cannot keep up with new malware threats
Solution Approach 1:
The patent performs comprehensive dataset preparation and model training in advance, creating pre-trained machine learning models that can be deployed for real-time detection. By completing the time-consuming data collection, feature extraction, and model training processes beforehand, the system eliminates the need for extensive offline processing when new bitstreams need to be analyzed, thus reducing detection time while maintaining high detection capability.
Data Source
AI summary
A method and apparatus for generating a dataset for training a content detection machine learning model. The method applies one or more transforms to a content containing bitstream that produce feature tensors representing the content, labels the feature tensors by type of content, stores feature tensors and labels in a dataset. The dataset my be used to train a content detection machine learning model. The model may be exported to content detectors to identify and classify bitstream content contained in other bitstreams.