Densely Connected Residual Network for Attack Recognition
Peilun Wu, Nour Moustafa, Shiyi Yang, Hui Guo
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.
Topics & Concepts
ResidualResidual neural networkComputer scienceConstant false alarm rateEnhanced Data Rates for GSM EvolutionFalse alarmArtificial intelligenceALARMEdge deviceCloud computingPattern recognition (psychology)AlgorithmEngineeringAerospace engineeringOperating systemNetwork Security and Intrusion DetectionAdvanced Malware Detection TechniquesAnomaly Detection Techniques and Applications