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Managing High Volume Data for Network Attack Detection Using Real-Time Flow Filtering
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作者 abhrajit ghosh Yitzchak M. Gottlieb +5 位作者 Aditya Naidu Akshay Vashist Alexander Poylisher Ayumu Kubota Yukiko Sawaya Akira Yamada 《China Communications》 SCIE CSCD 2013年第3期56-66,共11页
In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to hi... In this paper, we present Real-Time Flow Filter (RTFF) -a system that adopts a middle ground between coarse-grained volume anomaly detection and deep packet inspection. RTFF was designed with the goal of scaling to high volume data feeds that are common in large Tier-1 ISP networks and providing rich, timely information on observed attacks. It is a software solution that is designed to run on off-the-shelf hardware platforms and incorporates a scalable data processing architecture along with lightweight analysis algorithms that make it suitable for deployment in large networks. RTFF also makes use of state of the art machine learning algorithms to construct attack models that can be used to detect as well as predict attacks. 展开更多
关键词 network security intrusion detection SCALING
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