A Scalable Real-Time Framework for DDoS Traffic Monitoring and Characterization

被引：3

作者：

Huyn, Joojay ^{[1
]}

机构：

[1] Univ Calif Los Angeles, Dept Comp Sci, Los Angeles, CA 90024 USA

来源：

BDCAT'17: PROCEEDINGS OF THE FOURTH IEEE/ACM INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING, APPLICATIONS AND TECHNOLOGIES | 2017年

关键词：

D O I：

10.1145/3148055.3149205

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Volumetric DDoS attacks continue to inflict serious damage. Many proposed defenses for mitigating such attacks assume that a monitoring system has already detected the attack. However, many proposed DDoS monitoring systems do not focus on efficiently analyzing high volume network traffic to provide important characterizations of the attack in real-time to downstream traffic filtering systems. We propose a scalable real-time framework for an effective volumetric DDoS monitoring system that leverages modern big data technologies for streaming analytics of high volume network traffic to accurately detect and characterize attacks.

引用

页码：265 / 266

页数：2