TY - GEN
T1 - In-Depth Network Traffic Analysis using Machine Learning Perspective
T2 - 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023
AU - Sisilia Mukti, Fransiska
AU - Setijadi, Eko
AU - Affandi, Achmad
AU - Basuki, Achmad
AU - Ali Akbar, Muhammad
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - computational efficiency
KW - correlation analysis
KW - feature extraction
KW - feature selection
KW - machine learning
KW - network traffic analysis
UR - http://www.scopus.com/inward/record.url?scp=85190065542&partnerID=8YFLogxK
U2 - 10.1109/ISRITI60336.2023.10467384
DO - 10.1109/ISRITI60336.2023.10467384
M3 - Conference contribution
AN - SCOPUS:85190065542
T3 - 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding
SP - 415
EP - 421
BT - 6th International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2023 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 December 2023
ER -
