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3724 软件学报 2025 年第 36 卷第 8 期
10.1007/s11416-011-0151-y]
[7] Kong DG, Yan GH. Discriminant malware distance learning on structural information for automated malware classification. In: Proc. of
the 19th ACM SIGKDD Int’l Conf. on Knowledge Discovery and Data Mining. Chicago: ACM, 2013. 1357–1365. [doi: 10.1145/
2487575.2488219]
[8] Qiao YC, Zhang B, Zhang WZ. Malware classification method based on word vector of bytes and multilayer perception. In: Proc. of the
2020 IEEE Int’l Conf. on Communications. Dublin: IEEE, 2020. 1–6. [doi: 10.1109/ICC40277.2020.9149143]
[9] Raff E, Barker J, Sylvester J, Brandon R, Catanzaro B, Nicholas CK. Malware detection by eating a whole EXE. In: Proc. of the
Workshops of the 32nd AAAI Conf. on Artificial Intelligence. New Orleans: AAAI, 2018. 268–276.
[10] Arjovsky M, Bottou L, Gulrajani I, Lopez-Paz D. Invariant risk minimization. arXiv:1907.02893, 2020.
[11] Marcus G. Deep learning: A critical appraisal. arXiv:1801.00631, 2018.
[12] Nataraj L, Karthikeyan S, Jacob G, Manjunath BS. Malware images: Visualization and automatic classification. In: Proc. of the 8th Int’l
Symp. on Visualization for Cyber Security. Pittsburgh: ACM, 2011. 4. [doi: 10.1145/2016904.2016908]
[13] Kancherla K, Mukkamala S. Image visualization based malware detection. In: Proc. of the 2013 IEEE Symp. on Computational
Intelligence in Cyber Security. Singapore: IEEE, 2013. 40–44. [doi: 10.1109/CICYBS.2013.6597204]
[14] Gibert D, Mateu C, Planes J, Vicens R. Using convolutional neural networks for classification of malware represented as images. Journal
of Computer Virology and Hacking Techniques, 2019, 15(1): 15–28. [doi: 10.1007/s11416-018-0323-0]
[15] Jain M, Andreopoulos W, Stamp M. Convolutional neural networks and extreme learning machines for malware classification. Journal of
Computer Virology and Hacking Techniques, 2020, 16(3): 229–244. [doi: 10.1007/s11416-020-00354-y]
[16] Jang JW, Woo J, Yun J, Kim HK. Mal-netminer: Malware classification based on social network analysis of call graph. In: Proc. of the
23rd Int’l Conf. on World Wide Web. Seoul: ACM, 2014. 731–734. [doi: 10.1145/2567948.2579364]
[17] Kalash M, Rochan M, Mohammed N, Bruce NDB, Wang Y, Iqbal F. Malware classification with deep convolutional neural networks. In:
Proc. of the 9th IFIP Int’l Conf. on New Technologies, Mobility and Security. Paris: IEEE, 2018. 1–5. [doi: 10.1109/NTMS.2018.
8328749]
[18] Vasan D, Alazab M, Wassan S, Naeem H, Safaei B, Zheng Q. IMCFN: Image-based malware classification using fine-tuned
convolutional neural network architecture. Computer Networks, 2020, 171: 107138. [doi: 10.1016/j.comnet.2020.107138]
[19] Vasan D, Alazab M, Wassan S, Safaei B, Zheng Q. Image-based malware classification using ensemble of CNN architectures (IMCEC).
Computers & Security, 2020, 92: 101748. [doi: 10.1016/j.cose.2020.101748]
[20] Moskovitch R, Stopel D, Feher C, Nissim N, Elovici Y. Unknown malcode detection via text categorization and the imbalance problem.
In: Proc. of the 2008 IEEE Int’l Conf. on Intelligence and Security Informatics. Taipei: IEEE, 2008. 156–161. [doi: 10.1109/ISI.2008.
4565046]
[21] Jain S, Meena YK. Byte level n-gram analysis for malware detection. In: Proc. of the 5th Int’l Conf. on Information Processing.
Bangalore: Springer, 2011. 51–59. [doi: 10.1007/978-3-642-22786-8_6]
[22] Zhang FY, Zhao TZ. Malware detection and classification based on n-grams attribute similarity. In: Proc. of the 2017 IEEE Int’l Conf. on
Computational Science and Engineering (CSE) and IEEE Int’l Conf. on Embedded and Ubiquitous Computing. Guangzhou: IEEE, 2017.
793–796. [doi: 10.1109/CSE-EUC.2017.157]
[23] Mikolov T, Chen K, Corrado G, Dean J. Efficient estimation of word representations in vector space. In: Proc. of the 1st Int’l Conf. on
Learning Representations. Scottsdale: ICLR, 2013.
[24] Ahmadi M, Ulyanov D, Semenov S, Trofimov M, Giacinto G. Novel feature extraction, selection and fusion for effective malware family
classification. In: Proc. of the 6th ACM Conf. on Data and Application Security and Privacy. New Orleans: ACM, 2016. 183–194. [doi:
10.1145/2857705.2857713]
[25] Ni S, Qian Q, Zhang R. Malware identification using visualization images and deep learning. Computers & Security, 2018, 77: 871–885.
[doi: 10.1016/j.cose.2018.04.005]
[26] Sun GS, Qian Q. Deep learning and visualization for identifying malware families. IEEE Trans. on Dependable and Secure Computing,
2021, 18(1): 283–295. [doi: 10.1109/TDSC.2018.2884928]
[27] Awad Y, Nassar M, Safa H. Modeling malware as a language. In: Proc. of the 2018 IEEE Int’l Conf. on Communications. Kansas City:
IEEE, 2018. 1–6. [doi: 10.1109/ICC.2018.8422083]
[28] Kusner MJ, Sun Y, Kolkin NI, Weinberger KQ. From word embeddings to document distances. In: Proc. of the 32nd Int’l Conf. on
Machine Learning. Lille: JMLR.org, 2015. 957–966.
[29] Yan JQ, Yan GH, Jin D. Classifying malware represented as control flow graphs using deep graph convolutional neural network. In: Proc.
of the 49th Annual IEEE/IFIP Int’l Conf. on Dependable Systems and Networks. Portland: IEEE, 2019. 52–63. [doi: 10.1109/DSN.2019.

