  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.
