Automatically inferring patterns of resource consumption in network traffic

By Cristian Estan, Stefan Savage, George Varghese. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2003

332 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Mining anomalies using traffic feature distributions (2005, background): “There has also been considerable work on automatically finding clusters in single-link traffi c (not network-wide traffic, which is our focus); a notable example is [8].”
Data streams: algorithms and applications (2005, unknown): “A somewhat related problem concerns multidimensional signals.”
Sonata: query-driven streaming network telemetry (2018, methodology): “Autofocus [13], ProgME [57], and DREAM [29], SCREAM [30], MUL-TOPS [15], and HHH [23] all iteratively zoom in on traffic of interest.”
An empirical evaluation of entropy-based traffic anomaly detection (2008, background): “Other research efforts in the space of fine-grained anomaly detection have focused on measurements and analysis of backscatter and honeypot data [33], identifying heavy-hitters [5, 34], fast scanner detection [9] etc.”
Automated Worm Fingerprinting (2004, background): “Finally, it is reasonable to expect that the distribution of these addresses will be far more uniform than typical network traf(cid:2)c which can have signi(cid:2)cant clustering [9].”
BLINC: multilevel traffic classification in the dark (2005, methodology): “Such behaviors are also identiﬁable by tools like AutoFocus [6], which however do not target traﬃc classiﬁcation.”