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Adversarial examples against image-based malware classification systems

Vi, B.N. and Noi Nguyen, H. and Nguyen, N.T. and Truong Tran, C. (2019) Adversarial examples against image-based malware classification systems. In: 11th International Conference on Knowledge and Systems Engineering, KSE 2019, 24 October 2019 through 26 October 2019.

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Abstract

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. © 2019 IEEE.

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculties > Faculty of Information Technology
Identification Number: 10.1109/KSE.2019.8919481
Uncontrolled Keywords: Classification (of information); Convolution; Deep learning; Image classification; Neural networks; Systems engineering; Adversarial attack; Attack methods; Classification system; Classification technique; Convolution neural network; Convolutional neural network; Image-based; Malware classifications; Malware
Additional Information: Conference code: 155691. Language of original document: English.
URI: http://eprints.lqdtu.edu.vn/id/eprint/9250

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