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Relationship between link traffic and OD flow traffic is captured by the routing ... However, quite intuitively OD flows are correlated. ...
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A B C OD flow I-b Courtesy: Anukool Lakhina [Lakhina04] OD flow I-b Link c-b Link d-c Link f-d Link l-f Fri Sat Sun bt = At xt (t=1, ...
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Network-Wide Security Analysis. Anukool Lakhina. with Mark Crovella and Christophe Diot ... Working Hypothesis: Diagnosis & situational awareness requires a ...
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... Traffic Feature Distributions. Anukool Lakhina, Mark Crovella, Christophe Diot ... A general, unsupervised method for reliably detecting and classifying network ...
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Window-shopping for a bit not a bad idea. But, beware the 'many masters and no mentor' pitfall ... Remember the PhD is your gift to yourself. Sorry, I don't ...
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Network-Wide data we use: Traffic matrix views for Abilene and G ant at 10 min bins ... Worm. Point-Multipoint. 10. Automatically Classifying Anomalies [LCD:SIGCOMM05] ...
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to atlanta. to LA. from nyc. 8 ... separate normal & anomalous network-wide traffic ... Then, decompose traffic in all OD flows by projecting onto the two ...
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Increasing in sophistication: worm-compromised. hosts and bot-nets are massively distributed ... 1/1000 sampling, 10 min bins. Sprint European commercial network ...
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Three Disjoint Views of the Network. Policy: The operator's 'wish list' ... 'WorldCom Inc...suffered a widespread outage on its Internet backbone that ...
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'Ground truth' Time period: January 1, 2006 to June 30, 2006. Seattle. 6,766,986. Sunnyvale ... Destination Next-hop AS Path. 130.207.0.0/16. R1. 1..dest. R1 ...
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An Illustration. Detect. the time of the anomaly. Identify. the source ... A Geometric Illustration. In general, anomalous traffic results in a large value of ...
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Antibiotic Sensitivity Testing
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with Dina Papagiannaki, Mark Crovella, Christophe Diot, Eric Kolaczyk, and Nina Taft ... How does traffic move throughout the network? ...
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Pick k unique source nodes, uniformly at random ... When traces are run from few sources to large destinations, some portions of ...
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cars. UNC, 2005. C. Faloutsos. 7. School of Computer Science. Carnegie Mellon ... (and std - infinity (!)) degree. count. avg: 3.3. UNC, 2005. C. Faloutsos. 39 ...
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Boston U., 2005. C. Faloutsos. 1. School of Computer Science. Carnegie Mellon ... Boston U., 2005. C. Faloutsos. 2. School of Computer Science. Carnegie Mellon ...
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to atlanta. to LA. from nyc. 8. Data Collected. Collect sampled NetFlow data from all routers of: ... An approach to separate normal & anomalous network-wide traffic ...
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self-managing storage. infrastructure. a storage brick (0.5 5 TB) ~1 PB ' ... Self-* Storage (Ganger ) C. Faloutsos. 11. School of Computer Science. Carnegie Mellon ...
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Data Mining using Fractals (fractals for fun and profit) ... New tools: power laws, self-similarity and fractals' work, where traditional assumptions fail ...
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to seattle. to atlanta. to LA. from nyc. 10. Why Origin-Destination Flows? ... common (user-driven) daily and weekly cycles: 17. Outline for rest of talk ...
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Measurement tools are primitive. Technical challenges are significant. ... Continue to conduct measurements, build more informed models, and validate them. ...
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This talk: focus on detection and identification. 20 ... e.g., Spammers hijack route prefixes (network layer), to send spam at ...
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There is no reliable cyber attack early warning system and no reliable ... attack upon a large, complicated network disturbs the fractal nature of that ...
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