@inproceedings{5e6eb8962b9d457a8ea3c81abc077304,
title = "A convolutional transformation network for malware classification",
abstract = "{\textcopyright} 2019 IEEE.Modern malware evolves various detection avoidance techniques to bypass the state-of-the-art detection methods. An emerging trend to deal with this issue is the combination of image transformation and machine learning techniques to classify and detect malware. However, existing works in this field only perform simple image transformation methods that limit the accuracy of the detection. In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples. In particular, we first develop a novel hybrid image transformation method to convert binaries into color images that convey the binary semantics. The images are trained by a deep convolutional neural network that later classifies the test inputs into benign or malicious categories. Through the extensive experiments, our proposed method surpasses all baselines and achieves 99.14% in terms of accuracy on the testing set.",
author = "D.-L. Vu and T.-K. Nguyen and T.V. Nguyen and T.N. Nguyen and F. Massacci and P.H. Phung",
year = "2019",
month = dec,
day = "1",
doi = "10.1109/NICS48868.2019.9023876",
language = "English",
isbn = "9781728151632",
series = "Proceedings - 2019 6th NAFOSTED Conference on Information and Computer Science, NICS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "234--239",
editor = "{Quoc Bao}, V.N. and P.M. Quang and {Van Hoa}, H.",
booktitle = "Proceedings - 2019 6th NAFOSTED Conference on Information and Computer Science, NICS 2019",
address = "United States",
note = "6th NAFOSTED Conference on Information and Computer Science, NICS 2019 ; Conference date: 12-12-2019 Through 13-12-2019",
}