Abstract:In cyberspace, accurate, rapid and comprehensive network assets detection is the premise of realizing safe and effective management of digital assets, rather identification of operating system is the foundation of network asset detection. The identification of operating system information in traffic can take precautions again known vulnerabilities. This paper provides a kind of operating system fast fingerprint identification based on Convolution Neural Network, design and build the two-layer convolution model which uses ReLU function as the activation function and increased the BN layer, pooling layer, link layer, by the use of the traffic detection analysis tools p0f and take the fingerprint data of its fingerprint database operating system as the training set, The collected traffic data was used as a test set for fingerprint identification test, and the SVM method and decision tree method were combined with the model constructed in this paper for control experiment. Experimental results show that the proposed operating system recognition model has high convergence speed and accuracy, and the average discrimination accuracy is 13 and 6 percentage points higher than that of SVM algorithm and C4.5 decision tree algorithm, which proves that the proposed model has good performance in operating system recognition.