The accuracy of statistical methods for detecting a distributed denial-of-service (DDoS) attack improves with increasing packet sequence number (window size). However, such methods tend to suffer from low responsiveness. An alternative approach, real-time burst detection, offers two advantages over traditional statistical methods. First, it can be used for real-time detection when a DDoS event is occurring, and second, it can be judged with less processing as information about events can be compressed, even if a large number of events occur. Here, we propose a highly response burst detection method for DDoS attacks, perform experiments to evaluate its effectiveness, and discuss its detection accuracy and processing performance.
CITATION STYLE
Usuzaki, S., Arikawa, Y., Yamaba, H., Aburada, K., Kubota, S. I., Park, M., & Okazaki, N. (2018). Highly responsive distributed denial-of-service attacks detection by using real-time burst detection method. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 7, pp. 914–923). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-319-65521-5_82
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