Machine Learning For Traffic Analysis

Machine Learning for Traffic Analysis is a computational methodology that employs machine learning algorithms to automatically detect, classify, and predict network traffic patterns by learning from empirical traffic data, thereby enabling intelligent inference and decision-making in network monitoring, anomaly detection, and performance optimization.

1.9K

Publications

106.4K

Citations

5.8K

Authors

1.6K

Institutions

Publications per year

2017–2026

1.3K

Authors

5.8K

  1. AP
    Antonio Pescapè

    University of Naples Federico II, Italy

    20 publications · 1.5K citations

    17

    h-index

  2. GA
    Giuseppe Aceto

    University of Naples Federico II, Italy

    16 publications · 1.3K citations

    15

    h-index

  3. DC
    Domenico Ciuonzo

    University of Naples Federico II, Italy

    15 publications · 1.3K citations

    14

    h-index

  4. AN
    A. Nur Zincir‐Heywood

    Dalhousie University, Canada

    15 publications · 624 citations

    14

    h-index

  5. AM
    Antonio Montieri

    University of Naples Federico II, Italy

    13 publications · 1.2K citations

    13

    h-index

1–5 of 5.8K

Venues

  1. IEEE Access

    84 publications · 4.5K citations

    42

    h-index

  2. 32

    h-index

  3. IEEE Transactions on Network and Service Management

    TNSM · 33 publications · 2K citations

    26

    h-index

  4. 23

    h-index

  5. Sensors

    38 publications · 1.4K citations

    22

    h-index

1–5