AMON
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 monitoring is essential to network engineering, capacity planning and prevention / mitigation of threats. We develop an open source architecture, AMON (All-packet MONitor), 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 10Gbps+ 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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References in zbMATH (referenced in 3 articles )
Showing results 1 to 3 of 3.
Sorted by year (- Gao, Zheng; Stoev, Stilian: Fundamental limits of exact support recovery in high dimensions (2020)
- Kokoszka, Piotr; Nguyen, Hieu; Wang, Haonan; Yang, Liuqing: Statistical and probabilistic analysis of interarrival and waiting times of Internet2 anomalies (2020)
- Bhattacharya, Shrijita; Kallitsis, Michael; Stoev, Stilian: Data-adaptive trimming of the Hill estimator and detection of outliers in the extremes of heavy-tailed data (2019)