With the development of detection algorithms on malicious dynamic domain names, domain generation algorithms have developed to be more stealthy. The use of multiple elements for generating domains will lead to higher detection difficulty. To effectively improve the detection accuracy of algorithmically generated domain names based on multiple elements, a domain name syntax model is proposed, which analyzes the multiple elements in domain names and their syntactic relationship, and an adaptive embedding method is proposed to achieve effective element parsing of domain names. A parallel convolutional model based on the feature selection module combined with an improved dynamic loss function based on curriculum learning is proposed, which can achieve effective detection on multielement malicious domain names. A series of experiments are designed and the proposed model is compared with five previous algorithms. The experimental results denote that the detection accuracy of the proposed model for multiple-element malicious domain names is significantly higher than that of the comparison algorithms and also has good adaptability to other types of malicious domain names.

Traffic sign classification is the core of intelligent transportation and fundamental for constructing an automatic driving system. While supervised classification tasks demonstrate promising classification performance, a particular challenge is how to ensure the confidence for collecting labelled data. This paper presents a semi-supervised approach via label confidence for traffic sign classification to avoid the interference of uncertain labelled data. The idea of the proposed approach is to compare unsupervised information of the data, the supervised information carried by the learning data, and the supervised information which is given by the classification model in order to detect inconsistencies. The approach is able to build a robust classification model. Experimental results on benchmark and real-world dataset demonstrate that our approach significantly outperforms the existing approaches when uncertain labelled data exists.