1 |
Kim, H., Han, S., Lee, S., & Lee, J.-R., "Visualization of Malwares for Classification Through Deep Learning," Journal of Internet Computing and Services, 19(5), 67-75, 2018.
DOI
|
2 |
Biggio, Battista, et al., "Evasion attacks against machine learning at test time," in Proc. of Joint European conference on machine learning and knowledge discovery in databases, Springer, Berlin, Heidelberg, 387-402, 2013.
|
3 |
Zhu, Xinyue, et al., "Emotion classification with data augmentation using generative adversarial networks," in Proc. of Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Cham, 349-360, 2018.
|
4 |
G. Daniel, et al., "Using convolutional neural networks for classification of malware represented as images," Journal of Computer Virology and Hacking Techniques, 15.1, 15-28, 2019.
DOI
|
5 |
Anderson, Hyrum S., et al., "Evading machine learning malware detection," Black Hat, 2017.
|
6 |
Nguyen, Anh, Jason Yosinski, and Jeff Clune, "Deep neural networks are easily fooled: High confidence predictions for unrecognizable images," in Proc. of the IEEE conference on computer vision and pattern recognition, 2015.
|
7 |
Nataraj, Lakshmanan, et al., "Malware images: visualization and automatic classification," in Proc. of the 8th international symposium on visualization for cyber security, ACM, 1-7, 2011.
|
8 |
Kim, Jin-Young, Seok-Jun Bu, and Sung-Bae Cho, "Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders," Information Sciences, 460-461, 83-102, 2018.
DOI
|
9 |
Hwantae Ji, Eulgyu Im, "Malware Classfication Using Machine Learning and Binary Visualization," The Korean Institute of Information Scientists and Engineers, 24, 198-203, 2018.
|
10 |
Saxe, Joshua, and Konstantin Berlin, "Deep neural network based malware detection using two dimensional binary program features," in Proc. of 2015 10th International Conference on Malicious and Unwanted Software (MALWARE), IEEE, 2015.
|
11 |
Szegedy, Christian, et al., "Intriguing properties of neural networks," arXiv preprint arXiv: 1312.6199, 2013.
|
12 |
Papernot, Nicolas, et al., "The limitations of deep learning in adversarial settings," in Proc. of 2016 IEEE European Symposium on Security and Privacy (EuroS&P), IEEE, 2016.
|
13 |
Chen, Li, "Deep transfer learning for static malware classification," arXiv preprint arXiv:1812.07606, 2018.
|
14 |
Papernot, Nicolas, et al., "Distillation as a defense to adversarial perturbations against deep neural networks," in Proc. of 2016 IEEE Symposium on Security and Privacy (SP), IEEE, 2016.
|
15 |
Maaten, Laurens van der, and Geoffrey Hinton, "Visualizing data using t-SNE," Journal of machine learning research, 9, 2579-2605, 2008.
|
16 |
Goodfellow, Ian, et al., "Generative adversarial nets," Advances in neural information processing systems, 2014.
|
17 |
Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton, "ImageNet classification with deep convolutional neural networks," Advances in neural information processing systems, 2012.
|
18 |
Oliva, A. and Torralba. A, "Modeling the shape of a scene: a holistic representation of the spatial envelope," International Journal of Computer Vision, 42(3), 145-175, 2001.
DOI
|
19 |
Xiao, Chaowei, et al., "Generating adversarial examples with adversarial networks," arXiv preprint arXiv:1801.02610, 2018.
|
20 |
Radford, Alec, Luke Metz, and Soumith Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks," arXiv preprint arXiv:1511.06434, 2015.
|
21 |
Antoniou, Antreas, Amos Storkey, and Harrison Edwards, "Data augmentation generative adversarial networks," arXiv preprint arXiv:1711.04340, 2017.
|
22 |
Wang, Zhou, Eero P. Simoncelli, and Alan C. Bovik, "Multiscale structural similarity for image quality assessment," in Proc. of The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, Ieee, Vol. 2, 2003.
|
23 |
Ronen, Royi, et al., "Microsoft malware classification challenge," arXiv preprint arXiv:1802.10135, 2018.
|
24 |
Hanley, James A., and Barbara J. McNeil, "The meaning and use of the area under a receiver operating characteristic (ROC) curve," Radiology, 143.1, 29-36, 1982.
DOI
|
25 |
Bowles, Christopher, et al., "GAN augmentation: augmenting training data using generative adversarial networks," arXiv preprint arXiv:1810.10863, 2018.
|
26 |
Grosse, Kathrin, et al., "Adversarial examples for malware detection," in Proc. of European Symposium on Research in Computer Security, Springer, Cham, 62-79, 2017.
|
27 |
Kolosnjaji, Bojan, et al., "Adversarial malware binaries: Evading deep learning for malware detection in executables," in Proc. of 2018 26th European Signal Processing Conference (EUSIPCO), IEEE, 2018.
|
28 |
Hu, Weiwei, and Ying Tan, "Generating adversarial malware examples for black-box attacks based on GAN," arXiv preprint arXiv:1702.05983, 2017.
|
29 |
Odena, Augustus, Christopher Olah, and Jonathon Shlens, "Conditional image synthesis with auxiliary classifier gans," in Proc. of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.
|
30 |
Samangouei, Pouya, Maya Kabkab, and Rama Chellappa, "Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models," 2018.
|
31 |
Mariani, Giovanni, et al., "Bagan: Data augmentation with balancing gan," arXiv preprint arXiv:1803.09655, 2018.
|
32 |
Brodersen, Kay Henning, et al., "The balanced accuracy and its posterior distribution," in Proc. of 2010 20th International Conference on Pattern Recognition, IEEE, 2010.
|
33 |
Mirza, Mehdi, and Simon Osindero, "Conditional generative adversarial nets," arXiv preprint arXiv:1411.1784, 2014.
|
34 |
Kolosnjaji, Bojan, et al., "Adversarial malware binaries: Evading deep learning for malware detection in executables," in Proc. of 2018 26th European Signal Processing Conference (EUSIPCO), IEEE, 2018.
|
35 |
Goodfellow, Ian J., Jonathon Shlens, and Christian Szegedy, "Explaining and harnessing adversarial examples," arXiv preprint arXiv: 1412.6572, 2014.
|