Distributed denial-of-service is one kind of the most highlighted and most important attacks of today's cyberworld. With simple but extremely powerful attack mechanisms, it introduces an immense threat to current Internet community. In this article, we present a comprehensive survey of distributed denial-of-service attack, prevention, and mitigation techniques. We provide a systematic analysis of this type of attacks including motivations and evolution, analysis of different attacks so far, protection techniques and mitigation techniques, and possible limitations and challenges of existing research. Finally, some important research directions are outlined which require more attentions in near future to ensure successful defense against distributed denial-of-service attacks.

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Graphical methods are used for construction. Data analysis and visualization are an important area of applications of big data. At the same time, visual analysis is also an important method for big data analysis. Data visualization refers to data that is presented in a visual form, such as a chart or map, to help people understand the meaning of the data. Data visualization helps people extract meaning from data quickly and easily. Visualization can be used to fully demonstrate the patterns, trends, and dependencies of your data, which can be found in other displays. Big data visualization analysis combines the advantages of computers, which can be static or interactive, interactive analysis methods and interactive technologies, which can directly help people and effectively understand the information behind big data. It is indispensable in the era of big data visualization, and it can be very intuitive if used properly. Graphical analysis also found that valuable information becomes a powerful tool in complex data relationships, and it represents a significant business opportunity. With the rise of big data, important technologies suitable for dealing with complex relationships have emerged. Graphics come in a variety of shapes and sizes for a variety of business problems. Graphic analysis is first in the visualization. The step is to get the right data and answer the goal. In short, to choose the right method, you must understand each relative strengths and weaknesses and understand the data. Key steps to get data: target; collect; clean; connect.

With the rapid development of mobile Internet and finance technology, online e-commerce transactions have been increasing and expanding very fast, which globally brings a lot of convenience and availability to our life, but meanwhile, chances of committing frauds also come in all shapes and sizes. Moreover, fraud detection in online e-commerce transactions is not totally the same to that in the existing areas due to the massive amounts of data generated in e-commerce, which makes the fraudulent transactions more covertly scattered with genuine transactions than before. In this article, a novel scalable and comprehensive approach for fraud detection in online e-commerce transactions is proposed with majorly four logical modules, which uses big data analytics and machine learning algorithms to parallelize the processing of the data from a Chinese e-commerce company. Groups of experimental results show that the approach is more accurate and efficient to detect frauds in online e-commerce transactions and scalable for big data processing to obtain real-time property.

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