Abstract: An indoor target positioning method based on an improved convolutional neural network (CNN) model includes acquiring and preprocessing target camera serial interface (CSI) data of a to-be-positioned target and matching the preprocessed target CSI data with fingerprints in a positioning fingerprint database to obtain coordinate information of the to-be-positioned target. The generation method of the positioning fingerprint database includes: collecting indoor WiFi signals by a software defined radio (SDR) platform to obtain indoor CSI data corresponding to the WiFi signals, and preprocessing the indoor CSI data; partitioning the preprocessed indoor CSI data into a plurality of data subsets through a clustering algorithm; training an improved CNN model by the data subsets to obtain a trained improved CNN model; and generating the positioning fingerprint database by the trained improved CNN model and the preprocessed indoor CSI data.

Abstract: The disclosure provides a method for detecting and defending a Distributed Denial of Service attack in an SDN environment. The method includes: building data messages acquired as feature messages by a proxy module; sending the feature messages to a pre-built detection model to obtain a detection result; making a decision instruction based on the detection result; and performing control operations by the proxy module based on the decision instruction.

Abstract: An indoor target positioning method based on an improved convolutional neural network (CNN) model includes acquiring and preprocessing target camera serial interface (CSI) data of a to-be-positioned target and matching the preprocessed target CSI data with fingerprints in a positioning fingerprint database to obtain coordinate information of the to-be-positioned target. The generation method of the positioning fingerprint database includes: collecting indoor WiFi signals by a software defined radio (SDR) platform to obtain indoor CSI data corresponding to the WiFi signals, and preprocessing the indoor CSI data; partitioning the preprocessed indoor CSI data into a plurality of data subsets through a clustering algorithm; training an improved CNN model by the data subsets to obtain a trained improved CNN model; and generating the positioning fingerprint database by the trained improved CNN model and the preprocessed indoor CSI data.

Abstract: The disclosure provides a method for detecting and defending a Distributed Denial of Service attack in an SDN environment. The method includes: building data messages acquired as feature messages by a proxy module; sending the feature messages to a pre-built detection model to obtain a detection result; making a decision instruction based on the detection result; and performing control operations by the proxy module based on the decision instruction.