Biblio

In this paper, we quantify elements representing video features and we propose the bitrate prediction of compressed encoding video using deep learning. Particularly, to overcome disadvantage that we cannot predict bitrate of compression video by using Constant Rate Factor (CRF), we use deep learning. We can find element of video feature with relationship of bitrate when we compress the video, and we can confirm its possibility to find relationship through various deep learning techniques.

With the increase of the number of network threats, the knowledge graph is an effective method to quickly analyze the network threats from the mass of network security texts. Named entity recognition in network security domain is an important task to construct knowledge graph. Aiming at the problem that key Chinese entity information in network security related text is difficult to identify, a named entity recognition model in network security domain based on BERT-BiLSTM-CRF is proposed to identify key named entities in network security related text. This model adopts the BERT pre-training model to obtain the word vectors of the preceding and subsequent text information, and the obtained word vectors will be input to the subsequent BiLSTM module and CRF module for encoding and sorting. The test results show that this model has a good effect on the data set of network security domain. The recognition effect of this model is better than that of LSTM-CRF, BERT-LSTM-CRF, BERT-CRF and other models, and the F1=93.81%.

Cybersecurity has gradually become the public focus between common people and countries with the high development of Internet technology in daily life. The cybersecurity knowledge analysis methods have achieved high evolution with the help of knowledge graph technology, especially a lot of threat intelligence information could be extracted with fine granularity. But named entity recognition (NER) is the primary task for constructing security knowledge graph. Traditional NER models are difficult to determine entities that have a complex structure in the field of cybersecurity, and it is difficult to capture non-local and non-sequential dependencies. In this paper, we propose a cybersecurity entity recognition model CyberEyes that uses non-local dependencies extracted by graph convolutional neural networks. The model can capture both local context and graph-level non-local dependencies. In the evaluation experiments, our model reached an F1 score of 90.28% on the cybersecurity corpus under the gold evaluation standard for NER, which performed better than the 86.49% obtained by the classic CNN-BiLSTM-CRF model.

With cybersecurity situation more and more complex, data-driven security has become indispensable. Numerous cybersecurity data exists in textual sources and data analysis is difficult for both security analyst and the machine. To convert the textual information into structured data for further automatic analysis, we extract cybersecurity-related entities and propose a self-attention-based neural network model for the named entity recognition in cybersecurity. Considering the single word feature not enough for identifying the entity, we introduce CNN to extract character feature which is then concatenated into the word feature. Then we add the self-attention mechanism based on the existing BiLSTM-CRF model. Finally, we evaluate the proposed model on the labelled dataset and obtain a better performance than the previous entity extraction model.