Abstract

This study provides an in-depth exploration of Network Traffic Analysis (NTA) utilizing a Machine Learning (ML) perspective, focusing on both characterization and classification. The study initiates with a comprehensive examination of traffic behavior, allowing for the identification of patterns and the establishment of correlations among various attributes. Notably, flow duration is identified as a key label demonstrating a positive correlation with cumulative inter-arrival time in the forward direction. Following this, the traffic is classified into distinct categories, facilitating an evaluation of their priority in commu-nication. The comparative study of nine algorithms reveals that while random forest attains superior accuracy, the decision tree offers expedited computational efficiency. This research advances our understanding of NTA methodologies, providing insights into their applicability and effectiveness.

Original languageEnglish
Title of host publication6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages415-421
Number of pages7
ISBN (Electronic)9798350358346
DOIs
Publication statusPublished - 2023
Event6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Batam, Indonesia
Duration: 11 Dec 2023 → …

Publication series

Name6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding

Conference

Conference6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023
Country/TerritoryIndonesia
CityBatam
Period11/12/23 → …

Keywords

  • computational efficiency
  • correlation analysis
  • feature extraction
  • feature selection
  • machine learning
  • network traffic analysis

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