Visible to the public A Scalable Real-Time Framework for DDoS Traffic Monitoring and Characterization

TitleA Scalable Real-Time Framework for DDoS Traffic Monitoring and Characterization
Publication TypeConference Paper
Year of Publication2017
AuthorsHuyn, Joojay
Conference NameProceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
PublisherACM
Conference LocationNew York, NY, USA
ISBN Number978-1-4503-5549-0
Keywordsapache kafka, apache spark, composability, data mining, DDoS detection, ddos monitoring, distributed denial-of-service attacks, Human Behavior, Metrics, Network security, pubcrawl, relational database security, relational databases, resilience, Resiliency, streaming analytics
Abstract

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.

URLhttps://dl.acm.org/citation.cfm?doid=3148055.3149205
DOI10.1145/3148055.3149205
Citation Keyhuyn_scalable_2017