A. Lakhina

Publications31
h-index19
Citations6,712
Highly Influential Citations543
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BRITE: an approach to universal topology generation
TLDR
We propose a topology generation framework which improves the state of the art and is based on the design principles of representativeness, inclusiveness, and interoperability. Expand
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Diagnosing network-wide traffic anomalies
TLDR
In this paper we propose a general method to diagnose anomalies. Expand
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Mining anomalies using traffic feature distributions
TLDR
The increasing practicality of large-scale flow capture makes it possible to conceive of traffic analysis methods that detect and identify a large and diverse set of anomalies. Expand
  • 1,203
  • 84
  • PDF
Diagnosing network-wide traffic anomalies
TLDR
In this paper we propose a general method to diagnose anomalies. Expand
  • 538
  • 48
  • PDF
BRITE: Universal Topology Generation from a User''s Perspective
TLDR
We propose a topology generation framework which improves the state of the art and is based on design principles which include representativeness, inclusiveness, and interoperability. Expand
  • 344
  • 36
  • PDF
Structural analysis of network traffic flows
TLDR
We present the first analysis of complete sets of OD flow time-series, taken from two different backbone networks (Abilene and Sprint-Europe). Expand
  • 526
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Traffic matrices: balancing measurements, inference and modeling
TLDR
We provide a taxonomy of the kinds of models that may make use of partial flow measurement, based on the nature of the measurements used and the spatial, temporal, or spatio-temporal correlation exploited. Expand
  • 214
  • 29
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Sampling biases in IP topology measurements
TLDR
We show that when graphs are sampled using traceroute-like methods, the resulting degree distribution can differ sharply from that of the underlying graph. Expand
  • 344
  • 26
  • PDF
Characterization of network-wide anomalies in traffic flows
TLDR
We apply the subspace method to three types of sampled flow traffic in a large academic network: multivariate timeseries of byte counts, packet counts, and IP-flow counts. Expand
  • 420
  • 24
  • PDF
Detection and identification of network anomalies using sketch subspaces
TLDR
In this paper we show how one can use random aggregations of IP flows (i.e., sketches) to enable more precise identification of the underlying causes of anomalies. Expand
  • 217
  • 12
  • PDF