Densely Connected Residual Network for Attack Recognition
University of New South Wales（新南威尔士大学）；Australian Center for Cyber, University of New South Wales（新南威尔士大学澳大利亚网络中心）；Sangfor Technologies Inc.（深信服科技股份有限公司）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
