Malware Detection Dataset Selection Using Ranked Metadata for Neural Networks
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Solution Overview
Problem
Current malware detection datasets are not optimized for neural network training, leading to errors in malware detection and the need for improved methods and apparatuses to create more effective datasets.
Innovation Solution
A method and apparatus that analyze malware and clean files to determine file metadata, assign relevancy scores, and select the most relevant files to form a dataset for training machine learning-based malware detectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current malware datasets are used for neural network training, then malware detection can be performed, but detection accuracy is reduced due to non-optimized information
Solution Approach 1:
The patent transforms malware samples by changing their representation parameters from raw binary/byte sequences to numerical feature vectors that capture structural and behavioral characteristics. This parameter transformation optimizes the data for neural network training, directly resolving the contradiction between detection reliability and dataset quality by making the information format suitable for the intended application.
2Quantity of substance
If manual malware analysis is performed to create datasets, then malware samples can be identified, but the process is tedious and time-consuming
Solution Approach 1:
The system implements automated malware analysis where the malware detection apparatus itself performs the analysis of malware samples to extract features and create training datasets. This self-service approach eliminates the need for manual analyst intervention, enabling rapid processing of large quantities of malware samples without incurring manual labor time costs.
Solution Approach 2:
The patent replaces the manual mechanical process of malware analysis with an automated computational system. Neural networks and machine learning algorithms automatically analyze malware binaries, extract relevant features, and generate training datasets, substituting human analysts with automated mechanical systems that operate continuously without fatigue or time loss.
3Measurement precision
If character string matching is used for malware detection, then specific malware can be identified, but new or adapted malware cannot be detected
Solution Approach 1:
The system performs preliminary analysis of malware samples to extract structural features, control flow graphs, and behavioral characteristics before actual detection occurs. By pre-processing malware into optimized feature representations and training neural networks in advance, the system prepares detection capabilities that can generalize to new malware variants, resolving the contradiction between precise identification and adaptability to new threats.
Solution Approach 2:
The patent introduces neural networks as an intermediary between raw malware binaries and detection decisions. Instead of direct character string matching, the neural network intermediary learns to recognize patterns and features that indicate malware, enabling the system to detect both known and new malware variants through learned representations rather than exact string matches.
Data Source
AI summary
A method and apparatus for generating a malware detection dataset. The method accesses a database comprising malware files and metadata related to the files. The metadata is ranked and the rankings combined into a relevancy score. The most relevant files in the database are identified as malware samples. The malware samples and their related scores are stored in a malware detection dataset.