Diffusion Models for Malware Purification and Adversarial Samples
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Solution Overview
Problem
Existing malware detection systems struggle to identify obfuscated malware variants due to the discrete nature of text in natural language processing, and there is a lack of research on applying diffusion models in cybersecurity for adversarial purification and sample generation.
Innovation Solution
Implement diffusion models to generate adversarial samples of known malware files by altering their content without affecting functionality, and use these samples to train malware detectors, as well as apply diffusion models for adversarial purification to remove noise from input files before classification, thereby enhancing malware detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diffusion models are applied to natural language processing for adversarial purification, then malware detection accuracy is improved, but the discrete nature of text makes adaptation difficult
Solution Approach 1:
The patent applies parameter changes by transforming the discrete text data into a continuous latent space representation through an autoencoder. This allows the diffusion model to operate on continuous parameters derived from text, enabling the application of diffusion processes to natural language processing tasks for adversarial purification and malware detection.
2Quantity of substance
If diffusion models generate adversarial samples by altering malware content, then training data diversity is improved, but the altered content may affect malware functionality
Solution Approach 1:
The patent implements feedback by using a malware detector to evaluate the generated adversarial samples. The detector provides feedback on whether the generated samples maintain malware characteristics and functionality. This feedback loop allows the system to filter out samples that have been altered too much, ensuring they remain effective for training while maintaining their malicious functionality.
3Reliability
If diffusion models are used for adversarial purification, then robustness against obfuscated malware is improved, but computational complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training the diffusion model and autoencoder on a large corpus of malware samples before actual detection tasks. This pre-training phase captures the essential features and patterns of malware, allowing the model to perform adversarial purification more efficiently during actual detection without requiring complex real-time computations.
Data Source
AI summary
Methods and systems for applying a diffusion model to adversarial purification and generating adversarial samples in malware detection are disclosed. According to an example, a malware file is inputted to a diffusion model to obtain an adversarial sample by altering content of the malware file. The adversarial sample is further tested by a malware detector. In some examples, the content of an input file may be encoded prior to be processed by the diffusion model. If the malware detector can identify the adversarial sample as a malware file, the diffusion model is updated to further alter the content until the adversarial sample successfully deceives the malware detector. According to another example, an executable file is purified using a diffusion model prior to be inputted to a malware detector. The diffusion model may remove potential malware content from the executable file, thus improving the performance of the malware detector.