Martin F. Arlitt

Computer Networks and Communications · 2000–2012

A researcher in the Science corpus: 8 papers; trusts 13 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

Whom Martin F. Arlitt trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Subhabrata Sen
  2. Thomas Karagiannis
  3. Andrew William Moore
  4. Konstantina (Dina) Papagiannaki
  5. Oliver Spatscheck
  6. Matthew Roughan
  7. Sebastian Zander
  8. Anil K. Jain
  9. Patrick Haffner
  10. Michael I. Jordanzoubin

Who trusts Martin F. Arlitt overall

  1. Yaohua Sun
  2. Thuy T. T. Nguyen
  3. Jun Zhang
  4. Jeffrey Erman
  5. Christine Fricker
  6. Bhuvan Urgaonkar
  7. J. Kolter
  8. Jorge A. Balazs
  9. Hyunchul Kim
  10. Muhammad Usama

Papers from Martin F. Arlitt's perspective

Ordered by how strongly the researchers Martin F. Arlitt trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Toward the Accurate Identification of Network Applications (Passive and Active Network Measurement Conference)
  2. Accurate, scalable in-network identification of p2p traffic using application signatures (The Web Conference)
  3. Flow Clustering Using Machine Learning Techniques (Passive and Active Network Measurement Conference)
  4. Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification (ACM/SIGCOMM Internet Measurement Conference)
  5. Transport layer identification of P2P traffic (ACM/SIGCOMM Internet Measurement Conference)
  6. Profiling internet backbone traffic: behavior models and applications (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
  7. Internet traffic classification using bayesian analysis techniques (Measurement and Modeling of Computer Systems)
  8. BLINC: multilevel traffic classification in the dark (Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication)
  9. Analyzing peer-to-peer traffic across large networks (International Memory Workshop)
  10. Algorithms for Clustering Data

Their papers