论文标题
密集连接的残留网络以攻击识别
Densely Connected Residual Network for Attack Recognition
论文作者
论文摘要
较高的错误警报率和低检测率是未知威胁感知的主要粘附点。为了解决这些问题，在论文中，我们提出了一个密集连接的残留网络（密集 - 固定网络），以供攻击识别。密集的液压网构建了几个基本的残差单元，其中每个单元都由一系列通过广泛的连接组成。我们的评估表明，密集的响应网络可以准确地发现出现在边缘，雾和云层中的各种未知威胁，并同时保持误报率比现有算法要低得多。
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.
