Design of malicious domain name inspection method based on group convolutional neural network

QIU Yingyu; XU Qiang

doi:10.11805/tkyda2020412

[2] WOODBRIDGE J, ANDERSON H S, AHUJA A, et al. Predicting domain generation algorithms with long short ?term memory networks[R/OL]. Ithaca,Newyork,USA:ChronicleCornell. 2016. http://arxiv.org/abs/1611. 00791.

[3] YU B, GRAY D L, PAN J, et al. Inline DGA detection with deep networks[C]// 2017 IEEE International Conference on Data Mining Workshops. Orleans,LA,USA:IEEE, 2017:1-2.

[4] VINAYAKUMAR R, SOMAN K P, POORNACHANDRAN P, et al. Evaluating deep learning approaches to characterize and classify the DGAs at scale[J]. Journal of Intelligent & Fuzzy Systems, 2018,34(3):1265-1276.

[5] ANDERSON H S,WOODBRIDGE J,FILAR B. Deep DGA:adversarially?tuned domain generation and detection[C]// 2016 ACM Workshop on Artificial Intelligence and Security. Vienna,Austria:ACM, 2016:22-24.

[6] CURTIN Ryan R, GARDNER Andrew B, GRZONKOWSKI Slawomir, et al. Detecting DGA domains with recurrent neural networks and side information[C]// 14th International Conference on Availability, Reliability and Security. Canterbury, United Kingdom:ACM, 2019:111-114.

[12] SZEGEDY C,LIU W,JIA Y,et al. Going deeper with convolutions[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston,MA,USA:IEEE, 2015:1-9.