Profiling internet backbone traffic: behavior models and applications

By Kuai Xu, Zhi-Li Zhang, Supratik Bhattacharyya. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2005

427 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/ae363091a3afccc6111537abaa845ccf4d004d2d

How researchers see this paper

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

In Artificial Intelligence

1. Charu C. Aggarwal
2. Ling Huang
3. Mark Crovella
4. Michalis Faloutsos
5. Christophe Diot
6. Wei Xu
7. Anukool Lakhina
8. Andrew William Moore

In Computer Networks and Communications

1. Mark Crovella
2. Christophe Diot
3. Anukool Lakhina
4. Ling Huang
5. Michalis Faloutsos
6. Andrew William Moore
7. Jeffrey Erman
8. Anirban Mahanti

In Hardware and Architecture

1. Anirban Mahanti
2. Martin F. Arlitt
3. Jeffrey Erman
4. Stephen F. Gull
5. Tom Auld
6. Michalis Faloutsos
7. Christophe Diot
8. Andrew William Moore

Citations in the corpus

• An empirical evaluation of entropy-based traffic anomaly detection (2008, background): “An Empirical Evaluation of Entropy-based Traf.c Anomaly Detection George Nychis, Vyas Sekar, David G. Andersen, Hyong Kim, Hui Zhang {gnychis,kim}@ece.cmu.edu, {vyass,dga,hzhang}@cs.cmu.edu Carnegie Mellon University, Pittsburgh, PA, USA ABSTRACT Entropy-based approaches for anomaly detection are…”
• No NAT'd User Left Behind: Fingerprinting Users behind NAT from NetFlow Records Alone (2014, background): “Ta rg et User and Host Profiling [22], [21] [34], [35], [32], [4], [16], [24] [36], [12] Traffic and Application Profiling [3], [25], [28] [37], [23], [15], [29] [6] Information Leaks [19], [30], [5], [33] Device Profiling [17], [11], [8]”
• SubFlow: Towards practical flow-level traffic classification (2012, background): “BLINC [8], [18] classifies flows based on the number of connections created by different hosts.”
• Offline/realtime traffic classification using semi-supervised learning (2007, methodology): “[31] developed a methodology, based on data mining and informationtheoretic techniques, to discover functional and application behavioral patterns of hosts and the services used by the hosts.”
• BLINC: multilevel traffic classification in the dark (2005, background): “allel to our work, profiling of end-host communications in backbone Internet traffic has been proposed in [23].”
• Mining anomalies using traffic feature distributions (2005, background): “Finally, concurrent with our work, the authors of [35] also use entropy to summarize traffic feature distributions, with the goal to classify and profile traffic on a single backbone link.”
• A Survey on Internet Traffic Identification (2009, background): “Using similar arguments to [29], Lan and Heidemann [30] argue that understanding the characteristics of Internet flows”
• Bayesian Neural Networks for Internet Traffic Classification (2007, methodology): “[18] documents a procedure for profiling the general”
• Detecting large-scale system problems by mining console logs (2009, methodology): “Notice that as detailed in Xu et al. (2009a), the online detection precision is lowered by a number of am-”