This is a summary of the Consultative Committee for Thermometry (CCT) Key Comparison CCT-K3, i.e. the comparison of realizations of the fixed points of the International Temperature Scale of 1990 (ITS-90) over the range 83.8058 K to 933.473 K. The differences in the realizations of the various fixed points in this range of the ITS-90 and the uncertainties of those differences are given for the fifteen standards laboratories participating in the comparison.
Anomaly detection models based on deep learning come up against difficulties on the deployment in real scenarios such as generalization problem. The performance of the model based on specific dataset is not as good as expected in other scenarios. In order to avoid this problem, it is a feasible solution to collect network data from the target environment to train the model. This paper proposes a network data reinforcement method based on the multiclass variational autoencoder to complete training tasks with little amount data. In this paper, anomaly detection models based on MLP and CNN are designed, respectively, and validation experiments are carried out on the CICIDS-2018 dataset. Compared with unreinforced models, models based on this method get faster convergence speed during training. During evaluation, models based on this method achieve an average accuracy of 93.69%, while unreinforced models only get an average accuracy of 55.63%. In addition, this method provides competitive results on insufficient data compared with those existing models on sufficient data.
Network intrusion detection models based on deep learning encounter problems in the migration application. The performance is not as good as expected. In this paper, a network intrusion detection method based on domain confusion is proposed to improve the migration performance of the model. A domain confusion network is designed for feature transformation based on the idea of domain adaptation, mapping the traffic data in different network environments to the same feature space. Meanwhile, a regularizer is proposed to control the information loss in the mapping process to ensure that the transformed feature obtains enough information for intrusion detection. The experiment results show that the detection performance of the model in this paper is similar to or even better than the traditional models, and the migration performance in different network environments is better than the traditional models.
Network anomaly detection system (NADS) is one of the most important methods to maintain network system security. At present, network anomaly detection models based on deep learning have become a research hotspot in the area because of their advantage in processing high-dimensional data and excellent performance on detecting anomaly. However, most of the related research studies are based on supervised learning, which has strict requirements for dataset such as labels with high accuracy. However, there are some difficulties in obtaining a large amount of data with complete label message, thus seriously hindering the development and deployment of NADS based on DL. In this paper, we propose an unsupervised learning method to detect network anomaly, contrastive representation for network data (CRND). Based on contrastive learning, without label message, a qualified model is trained, providing more possibilities for the field. On CICIDS2018, the evaluation experiment proves that CRND can achieve 96.13% accuracy with only 200 items, and its F1-score reaches 0.96, which is far higher than that of other existing unsupervised learning methods. As fine-tuning is carried out, F1-score can reach a convergence level of 0.99, and the detection performance is the same as that of the detection model based on supervised learning.
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