A Malware Family Classification Method Based on the Point Cloud Model DGCNN
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-92708-0_13
Reference20 articles.
1. Wang, Y., Sun, Y., Liu, Z.: Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. (TOG) 38(5), 1–12 (2019)
2. Karbab, E.M.B., Debbabi, M.: Portable, data-driven malware detection using natural language processing and machine learning techniques on behavioral analysis reports. Dig. Invest. 28, 77–87 (2019)
3. Jindal, C., Salls, C., Aghakhani, H.: Neurlux: dynamic malware analysis without feature engineering. In: Proceedings of the 35th Annual Computer Security Applications Conference, pp. 444–455 (2019)
4. Chistyakov, A., Lobacheva, E., Kuznetsov, A.: Semantic Embeddings for Program Behavior Patterns. arXiv preprint arXiv:1804.03635 (2018)
5. San, C.C., Thwin, M.M.S., Htun, N.L.: Malicious software family classification using machine learning multi-class classifiers. In: Proceedings of the Sixth International Conference on Computational Science and Technology, pp. 423–433 (2019)
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