Structural analysis of network traffic flows

By Anukool Lakhina, Konstantina (Dina) Papagiannaki, Mark Crovella, Christophe Diot, Eric D. Kolaczyk, Nina Taft. SIGMETRICS '04/Performance '04, 2004

598 citations recorded by the source corpus.

Subfields: Building and Construction, Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/49b2b5d1b3f70cad6437ed192895317a0be7cfa7

How researchers see this paper

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

In Building and Construction

1. Christophe Diot
2. Anukool Lakhina
3. Mark Crovella

In Computer Networks and Communications

1. Mark Crovella
2. Christophe Diot
3. Anukool Lakhina
4. Blaine Nelson
5. Charu C. Aggarwal
6. Nina Taft
7. Benjamin I. P. Rubinstein
8. Anthony D. Joseph

In Statistical and Nonlinear Physics

1. Anukool Lakhina
2. Christophe Diot
3. Mark Crovella
4. Pierre Vandergheynst

Citations in the corpus

• A comprehensive survey on network anomaly detection (2018, unknown): “work with principal component analysis [10,69,70].”
• Diagnosing network-wide traffic anomalies (2004, methodology): “To apply the subspace method online, one processes each arrival of new traffic measurements using the matrix PP T , which is derived from the SVD. Previous work has shown that (for OD flows) this matrix can be reasonably stable from week to week [16].”
• Characterization of network-wide anomalies in traffic flows (2004, methodology): “In [16] we showed basic characteristics of the byte counts in OD flows, without examining packet or flow counts, and without concentrating on anomalies per se.”
• An Overview of IP Flow-Based Intrusion Detection (2010, unknown): “[58], [59], [60], [61].”