| 网络攻击检测中基于RTFF的大容量数据管理 |
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| 作者姓名： | Abhrajit Ghosh Yitzchak M. Gottlieb Aditya Naidu Akshay Vashist Alexander Poylisher Ayumu Kubota Yukiko Sawaya Akira Yamada |
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| 摘 要： |
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| 收稿时间： | 2013-03-27; |
Managing High Volume Data for Network Attack Detection Using Real-Time Flow Filtering |
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| Abhrajit Ghosh,Yitzchak M. Gottlieb,Aditya Naidu,Akshay Vashist,Alexander Poylisher,Ayumu Kubota,Yukiko Sawaya,Akira Yamada.Managing High Volume Data for Network Attack Detection Using Real-Time Flow Filtering[J].China communications magazine,2013,10(3):56-66. |
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| Authors: | Abhrajit Ghosh Yitzchak M Gottlieb Aditya Naidu Akshay Vashist Alexander Poylisher Ayumu Kubota Yukiko Sawaya Akira Yamada |
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| Affiliation: | 1Applied Communication Sciences, 150 Mount Airy Road, Basking Ridge, NJ 07920, USA
2KDDI R&D Laboratories, 2-1-15 Ohara Fujimino-shi, Saitama 356-8502, Japan |
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| Abstract: | 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 in-spection. 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 ob-served 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. |
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| Keywords: | network security intrusion detection scaling |
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