Feature Hashing for Malware Detection Index Vector
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Solution Overview
Problem
Existing methods for malware detection are inefficient due to lengthy data preprocessing and large training sets, which hinder the expedient analysis and feature extraction necessary for identifying malicious code.
Innovation Solution
The method employs feature hashing to generate an index vector from a feature set, reducing the feature space by hashing features and using sign attributes to avoid collisions, allowing for the creation of a smaller training dataset for machine learning models to identify malicious files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data preprocessing and feature extraction methods are used for malware detection, then comprehensive feature analysis can be achieved, but the preprocessing time becomes excessively long
Solution Approach 1:
The patent extracts only the most frequent and discriminative features from the complete feature set, rather than processing all features. By identifying and retaining only the top-k frequent features that contribute most to malware detection accuracy, the system achieves comprehensive analysis of critical features while dramatically reducing preprocessing time.
Solution Approach 2:
The patent applies partial action by processing only a subset of features (the most frequent ones) rather than the entire feature set. This selective approach processes approximately the top 10-20% of features that provide the most value for detection, achieving sufficient detection accuracy with significantly reduced computational overhead.
2Reliability
If complete feature sets are used for training machine learning models, then detection accuracy can be maximized, but the training set size becomes unmanageably large
Solution Approach 1:
The patent extracts and retains only the most discriminative features for model training by identifying the top-k frequent features across the training data. This extraction process eliminates redundant and less informative features, resulting in a compact feature set that maintains detection accuracy while reducing training set size to manageable levels.
Solution Approach 2:
Instead of starting with complete feature sets and attempting to reduce them, the patent inverts the approach by first identifying the most frequent and informative features, then building the training set around this condensed feature subset. This inversion naturally produces a smaller, more efficient training set from the outset.
3Measurement precision
If all features are processed and analyzed, then thorough malware identification can be achieved, but the computational complexity increases significantly
Solution Approach 1:
The patent segments the feature processing task into two distinct phases: (1) frequency-based feature selection to identify the most informative features, and (2) model training and detection using only this selected subset. This segmentation divides the complex task of processing all features into manageable stages, reducing overall computational complexity while maintaining identification thoroughness.
Solution Approach 2:
The patent applies partial action by focusing computational resources on processing only the most frequent and informative features rather than all features equally. This selective processing achieves thorough malware identification for the critical feature subset while avoiding the excessive computational complexity of analyzing every possible feature.
Data Source
AI summary
Data is analyzed using feature hashing to detect malware. A plurality of features in a feature set is hashed. The feature set is generated from a sample. The sample includes at least a portion of a file. Based on the hashing, one or more hashed features are indexed to generate an index vector. Each hashed feature corresponds to an index in the index vector. Using the index vector, a training dataset is generated. Using the training dataset, a machine learning model for identifying at least one file having a malicious code is trained.