| 摘要(英) |
In the recent years, global investment in FinTech increase rapidly. Some financial companies use technology to improve their service. For example, the retail banking website
of J.P. Morgan Chase & Co. (JPM) allows customers to make online loans through their website. Because of the development, financial companies have to face more cyberattacks than before. One of the famous attack is Distributed Denial of Service (DDoS) attack. It is used to shot down the servers of financial companies, so that network services can not be used.
In order to detect DDoS attack, we propose a method to detect the distribution changes in a fraction of data streams under a large number of detectors. It can be seem as monitoring a large number of routers at the same time, and each router receives network traffic. When hacker launch an attack, a fraction of routers will receive abnormal traffic. Our task is to detect the change as quickly as possible, to reduce the loss. In this paper,
we show how the nearly optimal detection delay depends on the fraction of data streams undergoing distribution changes as the number of detectors goes to infinity. There are three detection domains. In the first domain for moderately large fractions, immediate detection is possible. In the second domain for smaller fractions, the detection delay grows logarithmically with the number of detectors. In the third domain for even smaller fractions. |
| 參考文獻 |
References
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31
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