AI Malware Detection via Binary Image Conversion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current malware detection methods, such as signature analysis and machine learning, are limited in detecting unknown malware and rely on patterns in accumulated data, failing to effectively classify malware binary data.
Innovation Solution
An AI-based malware detection system that converts malware binary data into image data using image technology and applies deep learning-based image recognition, involving metadata extraction, conversion to grayscale or spectrogram images, data augmentation, and training with Convolutional Neural Networks to classify malware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If signature analysis is used for malware detection, then detection accuracy for known malware is improved, but detection capability for unknown malware deteriorates
Solution Approach 1:
The patent transforms malware binary data into image data, adding a visual dimension to malware analysis. This dimensional transformation enables the use of image processing techniques and deep learning models that can detect patterns beyond traditional signature matching, thereby improving detection of unknown malware while maintaining accuracy for known malware.
Solution Approach 2:
The patent changes the representation parameters of malware data from binary format to image format with various visual features. By converting metadata into visual characteristics such as histograms, spectrograms, and other image representations, the system enables detection of previously undetectable malware patterns through visual analysis.
2Adaptability or versatility
If machine learning technology is introduced to detect unknown malware, then detection rate for unknown malware is improved, but reliance on accumulated data patterns limits effectiveness
Solution Approach 1:
The patent performs preliminary transformation of malware data into image format before analysis, extracting visual features and metadata that capture essential characteristics. This preliminary action enables the model to learn from visual patterns rather than relying solely on accumulated binary data patterns, improving generalization to unknown malware.
Solution Approach 2:
The patent replaces traditional machine learning approaches that rely on statistical patterns in accumulated data with deep learning models that process visual representations. This substitution enables the system to learn hierarchical features from image data, reducing dependence on pre-accumulated training data patterns.
3Productivity
If malware binary data is converted to image data, then deep learning-based classification effectiveness is improved, but data processing complexity increases
Solution Approach 1:
The patent segments the malware analysis process into distinct stages: metadata extraction, image conversion, feature processing, and classification. By dividing the complex transformation process into manageable segments, the system achieves high classification effectiveness while controlling processing complexity through modular architecture.
Solution Approach 2:
The patent introduces image data as an intermediary representation between raw binary malware data and the deep learning classifier. This intermediary format serves as a bridge that enables effective classification while managing complexity, as the image representation captures essential features in a structured format suitable for visual processing algorithms.
Data Source
AI summary
An AI-based malware detection method is provided. The method includes inputting malware binary data, extracting metadata from the inputted malware binary data, converting the extracted metadata into image data, and training a neural network on the converted image data to classify malware. Malware binary data can be effectively classified by converting the binary data to image data and analyzed through deep learning-based image models. In addition, results from the AI detection algorithm technology can be displayed visually for easy interpretation.