[1]Nataraj L, Karthikeyan S, Jacob G, et al. Malware images: Visualization and automatic classification[C] Proc of the 8th Int Symp on Visualization for Cyber Security. New York: ACM, 2011: 17[2]Shaid S. Malware behavior image for malware variant identification[C] Proc of 2014 Int Symp on Biometrics and Security Technologies (ISBAST). Piscataway, NJ: IEEE, 2014: 238243[3]Tobiyama S, Yamaguchi Y, Shimada H, et al. Malware detection with deep neural network using process behavior[C] Proc of the 40th IEEE Annual Computer Software and Applications Conf (COMPSAC). Piscataway, NJ: IEEE, 2016: 577582[4]Cui Z , Xue F , Cai X , et al. Detection of malicious code variants based on deep learning[J]. IEEE Trans on Industrial Informatics, 2018, 14(7): 31873196[5]Vasan D, Alazab M, Wassan S, et al. IMCFN: Imagebased malware classification using finetuned convolutional neural network architecture[J]. Computer Networks, 2020, 171: 107138[6]Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C] Proc of the 31st Int Conf on Neural Information Processing Systems. New York: ACM, 2017: 60006010[7]Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: Transformers for image recognition at scale[J]. arXiv preprint, arXiv: 2010.11929, 2020[8]Arp D, Spreitzenbarth M, M Hübner, et al. DREBIN: Effective and explainable detection of Android malware in your pocket[C] Proc of Network & Distributed System Security Symp. San Diego, CA: The Internet Society, 2014: 2326[9]Taheri L, Kadir A, Lashkari A H. Extensible Android malware detection and family classification using networkflows and APIcalls[C] Proc of 2019 Int Carnahan Conf on Security Technology (ICCST). Piscataway, NJ: IEEE, 2019: 18
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