FPGA-Based Network Traffic Classification Using Machine Learning
Real-time classification of internet traffic is critical for the efficient management of networks. Classification approaches based on machine learning techniques have shown promising results with high levels of accuracy. In this paper, the suitability of packet-level and flow-level features is valid...
Saved in:
| Main Author: | Elnawawy, Mohammed (author) |
|---|---|
| Other Authors: | Sagahyroon, Assim (author), Shanableh, Tamer (author) |
| Format: | article |
| Published: |
2020
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/11073/19796 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
FPGA-Based Network Traffic Classification Using Machine Learning
by: Elnawawy, Mohammed
Published: (2019) -
A Novel Partitioned Random Forest Method-Based Facial Emotion Recognition
by: Hanif Heidari (22467148)
Published: (2025) -
Enhancing Bearing Fault Diagnosis Using Transfer Learning and Random Forest Classification: A Comparative Study on Variable Working Conditions
by: Durjay Saha (21633095)
Published: (2024) -
A Chatbot Intent Classifier for Supporting High School Students
by: K. Assayed, Suha
Published: (2023) -
FPGA-based Parallel Hardware Architecture for Real-time Object Classification
by: Qasaimeh, Murad Mohammad
Published: (2014)