- 著者
- Shotaro Usuzaki Yuki Arikawa Hisaaki Yamaba Kentaro Aburada Shin-Ichiro Kubota Mirang Park Naonobu Okazaki
- 出版者
- Information Processing Society of Japan
- 雑誌
- Journal of Information Processing (ISSN:18826652)
- 巻号頁・発行日
- vol.26, pp.257-266, 2018 (Released:2018-03-15)
- 参考文献数
- 18
Distributed Denial-of-Service (DDoS) attack detection systems are classified into a signature based approach and an anomaly based approach. However, such methods tend to suffer from low responsiveness. On the other hand, real-time burst detection which is used in data mining offers two advantages over traditional statistical methods. First, it can be used for real-time detection when an event is occurring, and second, it can work with less processing as information about events are compressed, even if a large number of events occur. Here, the authors add the function for attack detection in real-time burst detection technique, and propose a highly responsive DDoS attack detection technique. This paper performs experiments to evaluate its effectiveness, and discusses its detection accuracy and processing performance.