Adversarial Examples Against Image-based Malware Classification Systems
| Autor: | Huu Noi Nguyen, Ngoc Tran Nguyen, Bao Ngoc Vi, Cao Truong Tran |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Deep learning 020206 networking & telecommunications 02 engineering and technology computer.software_genre Machine learning Convolutional neural network Visualization Adversarial system ComputingMethodologies_PATTERNRECOGNITION Robustness (computer science) 0202 electrical engineering electronic engineering information engineering Malware 020201 artificial intelligence & image processing Artificial intelligence business computer Image based |
| Zdroj: | KSE |
| Popis: | Malicious software, known as malware, has become urgently serious threat for computer security, so automatic mal-ware classification techniques have received increasing attention. In recent years, deep learning (DL) techniques for computer vision have been successfully applied for malware classification by visualizing malware files and then using DL to classify visualized images. Although DL-based classification systems have been proven to be much more accurate than conventional ones, these systems have been shown to be vulnerable to adversarial attacks. However, there has been little research to consider the danger of adversarial attacks to visualized image-based malware classification systems. This paper proposes an adversarial attack method based on the gradient to attack image-based malware classification systems by introducing perturbations on resource section of PE files. The experimental results on the Malimg dataset show that by a small interference, the proposed method can achieve success attack rate when challenging convolutional neural network malware classifiers. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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