Interpretable Rules with a Simplified Data Representation - a Case Study with the EMBER Dataset
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-53552-9_1
Reference9 articles.
1. Svec, P., Balogh, S., Homola, M.: Experimental evaluation of description logic concept learning algorithms for static malware detection. In: ICISSP 2021, pp. 792–799 (2021)
2. Mojžiš, J.: On the possibility of interpretable rules generation for the classification of malware samples. Industry 4.0. 7(6), 248–250 (2022)
3. Trizna, D.: Quo Vadis: hybrid machine learning meta-model based on contextual and behavioral malware representations. In: Proceedings of the 15th ACM Workshop on Artificial Intelligence and Security, pp. 127–136, 11 November 2022
4. Aggarwal, P., Ahamed, S.F., Shetty, S., Freeman, L.J.: Selective targeted transfer learning for malware classification. In: 2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 114–120. IEEE, 13 December 2021
5. Oyama, Y., Miyashita, T., Kokubo, H.: Identifying useful features for malware detection in the ember dataset. In: 2019 seventh international symposium on computing and networking workshops (CANDARW), pp. 360–366. IEEE, 26 November 2019
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