Densely Connected Residual Network for Attack Recognition
Cryptography and Security
2020-08-06 v1 Machine Learning
Abstract
High false alarm rate and low detection rate are the major sticking points for unknown threat perception. To address the problems, in the paper, we present a densely connected residual network (Densely-ResNet) for attack recognition. Densely-ResNet is built with several basic residual units, where each of them consists of a series of Conv-GRU subnets by wide connections. Our evaluation shows that Densely-ResNet can accurately discover various unknown threats that appear in edge, fog and cloud layers and simultaneously maintain a much lower false alarm rate than existing algorithms.
Cite
@article{arxiv.2008.02196,
title = {Densely Connected Residual Network for Attack Recognition},
author = {Peilun Wu and Nour Moustafa and Shiyi Yang and Hui Guo},
journal= {arXiv preprint arXiv:2008.02196},
year = {2020}
}