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Lee, M.J. Jo, and J.S. Shin, \u201cLigeroav: A light-weight, signature-based antivirus for mobile environment,\u201d IEICE Trans. Inf. & Syst., vol.E99-D, no.12, pp.3185-3187, Dec. 2016. 10.1587\/transinf.2016edl8163","DOI":"10.1587\/transinf.2016EDL8163"},{"key":"2","unstructured":"[2] V. Harrison and J. Pagliery, \u201cNearly 1 million new malware threats released every day,\u201d http:\/\/money.cnn.com\/2015\/04\/14\/technology\/security\/cyber-attack-hacks-security\/."},{"key":"3","unstructured":"[3] McAfee, McAfee Labs Threats Report, Dec. 2018."},{"key":"4","unstructured":"[4] J. Scott, Signature Based Malware Detection is Dead, Institute for Critical Infrastructure Technology, 2017."},{"key":"5","unstructured":"[5] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d ICLR 2015, San Diego, CA, May 2015."},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d CVPR 2016, Las Vegas, NV, pp.770-778, June 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"7","unstructured":"[7] L. Nataraj, S. Karthikeyan, G. Jacob, and B.S. Manjunath, \u201cMalware images: visualization and automatic classification,\u201d VizSec 2011, Pittsburgh, PA, pp.4:1-4:7, July 2011. 10.1145\/2016904.2016908"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] E. Rezende, G. Ruppert, T. Carvalho, A. Theophilo, F. Ramos, and P.D. Geus, \u201cMalicious software classification using vgg16 deep neural network's bottleneck features,\u201d Information Technology-New Generations, vol.738, pp.51-59, 2018. 10.1007\/978-3-319-77028-4_9","DOI":"10.1007\/978-3-319-77028-4_9"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] R.U. Khanz, X. Zhang, and R. Kumar, \u201cAnalysis of resnet and googlenet models for malware detection,\u201d Journal of Computer Virology and Hacking Techniques, vol.15, no.1, pp.29-37, 2019. 10.1007\/s11416-018-0324-z","DOI":"10.1007\/s11416-018-0324-z"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, \u201cGoing deeper with convolutions,\u201d CVPR 2015, Boston, MA, pp.1-9, June 2015. 10.1109\/cvpr.2015.7298594","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] R. Kumar, Z. Xiaosong, R.U. Khan, I. Ahad, and J. Kumar, \u201cMalicious code detection based on image processing using deep learning,\u201d 2018 International Conference on Computing and Artificial Intelligence, pp.81-85, 2018. 10.1145\/3194452.3194459","DOI":"10.1145\/3194452.3194459"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] D. Gibert, C. Mateu, J. Planes, and R. Vicens, \u201cUsing convolutional neural networks for classification of malware represented as images,\u201d Journal of Computer Virology and Hacking Techniques, vol.15, no.1, pp.15-28, 2019. 10.1007\/s11416-018-0323-0","DOI":"10.1007\/s11416-018-0323-0"},{"key":"13","unstructured":"[13] Korea Internet and Security Agency (KISA), \u201cData Challenge 2018,\u201d http:\/\/datachallenge.kr\/challenge18\/malware\/."},{"key":"14","unstructured":"[14] R. Ronen, M. Radu, C. Feuerstein, E. Yom-Tov, and M. Ahmadi, \u201cMicrosoft malware classification challenge,\u201d arXiv:1804.10135, Feb. 2018."},{"key":"15","unstructured":"[15] H.S. Anderson and P. Roth, \u201cEmber: An open dataset for training static pe malware machine learning models,\u201d arXiv:1804.04637, April 2018."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] M. Sebasti\u00e1n, R. Rivera, P. Kotzias, and J. Caballero, \u201cAvclass: A tool for massive malware labeling,\u201d International Symposium on Research in Attacks, Intrusions, and Defenses, vol.9854, pp.230-253, Springer, 2016. 10.1007\/978-3-319-45719-2_11","DOI":"10.1007\/978-3-319-45719-2_11"},{"key":"17","unstructured":"[17] Mal2D. https:\/\/github.com\/minkcho\/mal2d."},{"key":"18","unstructured":"[18] Keras team, https:\/\/github.com\/keras-team\/keras."},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] T. Saito and M. 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