AMON: An Open Source Architecture for Online Monitoring, Statistical Analysis, and Forensics of Multi-Gigabit Streams
The Internet, as a global system of interconnected networks, carries an extensive array of information resources and services. Key requirements include good quality-of-service and protection of the infrastructure from nefarious activity [e.g., distributed denial of service (DDoS) attacks]. Network m...
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Veröffentlicht in: | IEEE journal on selected areas in communications 2016-06, Vol.34 (6), p.1834-1848 |
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Sprache: | eng |
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Zusammenfassung: | The Internet, as a global system of interconnected networks, carries an extensive array of information resources and services. Key requirements include good quality-of-service and protection of the infrastructure from nefarious activity [e.g., distributed denial of service (DDoS) attacks]. Network monitoring is essential to network engineering, capacity planning, and prevention/mitigation of threats. We develop an open-source architecture, All-packet MONitor (AMON), for online monitoring and analysis of multi-gigabit network streams. It leverages the high-performance packet monitor PF_RING and is readily deployable on commodity hardware. AMON examines all packets, partitions traffic into sub-streams by using rapid hashing and computes certain real-time data products. The resulting data structures provide views of the intensity and connectivity structure of network traffic at the time-scale of routing. The proposed integrated framework includes modules for the identification of heavy-hitters as well as for visualization and statistical detection at the time-of-onset of high-impact events such as DDoS. This allows operators to quickly visualize and diagnose attacks, and limit offline and time-consuming post-mortem analysis. We demonstrate our system in the context of real-world attack incidents, and validate it against state-of-the-art alternatives. AMON has been deployed and is currently processing multi-gigabit live Internet traffic at Merit Network. It is extensible and allows the addition of further statistical and filtering modules for real-time forensics. |
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ISSN: | 0733-8716 1558-0008 |
DOI: | 10.1109/JSAC.2016.2558958 |