Distributed Denial of Service （DDoS） attacks， as a highly destructive type of cyber attacks， have become one of the most severe threats and challenges in the field of cybersecurity in recent years due to their low attack costs， high attack efficiency， and strong concealment. DDoS attacks employ a distributed control approach to mix malicious traffic with legitimate network requests， making it difficult for traditional security defense mechanisms such as Intrusion Detection System （IDS） and firewalls to identify and mitigate such attacks effectively. Consequently， the efficient detection and effective defense against DDoS attacks have become research hotspots and difficulties in the field of cybersecurity. Based on systematic survey of the existing research on DDoS attacks， the following was performed. Firstly， the classification methods of DDoS attacks were sorted out， and DDoS attacks were summed up from multiple perspectives， so as to provide a deeper understanding of DDoS attack mechanisms. Secondly， an analysis of the current development of DDoS attacks was conducted， with particular focuses on discussing the development trends in attack intensity， attack methods， and attack distribution， thereby providing support for the research on more efficient DDoS defense technologies. Thirdly， an in-depth analysis and evaluation of the status of DDoS attack defense technologies was conducted from both industrial and academic perspectives， which focused on DDoS detection and defense methods based on programmable switches and machine learning in the academic aspect， and compared and analyzed the defense architectures adopted by different participants in DDoS defense in the industrial aspect as well as summarized the technical characteristics， application scenarios， and the existing challenges of the architecture. Finally， based on a comprehensive analysis of the current DDoS attack situations， the future development directions， opportunities， and challenges of DDoS defense technology were prospected， providing new ideas and directions for researchers in the field of cybersecurity and promoting further innovation and development of DDoS defense technology.

Hypergraph is the generalization of graph， which has significant advantages in representing higher-order features of complex relationships compared with ordinary graph. As a relatively new data structure， hypergraph is playing a crucial role in various application fields increasingly. By appropriately using hypergraph models and algorithms， specific problems in real world were modeled and solved with higher efficiency and quality. Existing surveys of hypergraph mainly focus on the theory and techniques of hypergraph itself， and lack of a summary of modeling and solving methods in specific scenarios. To this end， after summarizing and introducing some fundamental concepts of hypergraph， the application methods， techniques， common issues， and solutions of hypergraph in various application scenarios were analyzed； by summarizing the existing work， some problems and obstacles that still exist in the applications of hypergraph to real-world problems were elaborated. Finally， the future research directions of hypergraph applications were prospected.

Sentiment analysis， as a subdivision of Natural Language Processing（NLP）， has experienced the development of using sentiment lexicon， machine learning and deep learning to analyze. According to the problem of low accuracy， over fitting phenomenon in training process and low coverage， large workload when compiling the sentiment lexicon when using the generalized deep learning model as a text classifier to analysis of Web text reviews in a specific field， a sentiment analysis model based on sentiment lexicon and stacked residual Bidirectional Long Short-Term Memory （Bi-LSTM） network was proposed. Firstly， the sentiment words in the sentiment lexicon were designed to cover the professional words in the research field of "educational robot"， thereby making up for the lack of accuracy of Bi-LSTM model in analyzing such texts. Then， Bi-LSTM and SnowNLP were used to reduce the volume of compilation of the sentiment lexicon. The memory gate and forget gate structures of Long Short-Term Memory （LSTM） network were able to ensure that the relevance of the words before and after in the comment text were fully considered with some analyzed words selected to be forgotten at the same time， thereby avoiding the problem of gradient explosion during the back propagation. After the introduction of the stacked residual Bi-LSTM， not only the number of layers of the model was deepened to 8， but also the "degradation" problem caused by the residual network stacking LSTM was avoided. Finally， by setting and adjusting the score weights of the two parts appropriately， and the sigmoid activation function was used to normalize the total score to the interval of ［0，1］. According to the interval division of ［0，0.5］ and （0.5，1］， negative and positive emotions were represented respectively， and sentiment classification was completed. Experimental results show that the sentiment classification accuracy of the proposed classification model for the reviews dataset about "educational robot" is improved by about 4.5 percentage points compared with the standard LSTM model and by about 2.0 percentage points compared with the BERT （Bidirectional Encoder Representation from Transformers）. In conclusion， the sentiment classification model based on sentiment lexicon and deep learning classification model was generalized by the proposed model， and by modifying the sentiment words in the lexicon and appropriately adjusting the layer number and the structure of the deep learning model， the proposed model can be applied to accurate sentiment analysis of shopping reviews of all kinds of goods in e-commerce platform， thereby helping enterprises to understand the consumers’ shopping psychology and the market demand， as well as providing consumers with a reference standard for the quality of goods.