scholarly journals Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

2021 ◽  
Vol 170 ◽  
pp. 19-41 ◽  
Author(s):  
Mahmoud Abbasi ◽  
Amin Shahraki ◽  
Amir Taherkordi
Keyword(s):  
Deep Learning ◽  
Network Traffic ◽  
Traffic Monitoring ◽  
Network Traffic Monitoring

Adaptive Multiagent System for Network Traffic Monitoring

2009 ◽  
Vol 24 (3) ◽  
pp. 16-25 ◽  
Author(s):  
Martin Rehák ◽  
Michal Pechoucek ◽  
Martin Grill ◽  
Jan Stiborek ◽  
Karel Bartoš ◽  
...  
Keyword(s):  
Network Traffic ◽  
Multiagent System ◽  
Traffic Monitoring ◽  
Network Traffic Monitoring

Netpy: Advanced Network Traffic Monitoring

2009 ◽  
Author(s):  
Andreea Cirneci ◽  
Stefan Boboc ◽  
Catalin Leordeanu ◽  
Valentin Cristea ◽  
Cristian Estan
Keyword(s):  
Network Traffic ◽  
Traffic Monitoring ◽  
Network Traffic Monitoring

Implementation of Network Traffic Monitoring using Software Defined Networking Ryu Controller

2021 ◽  
Vol 16 ◽  
pp. 270-277
Author(s):  
Omran M. A. Alssaheli ◽  
Z. Zainal Abidin ◽  
N. A. Zakaria ◽  
Z. Abal Abas
Keyword(s):  
Network Traffic ◽  
Network Architecture ◽  
Network Performance ◽  
Software Defined Networking ◽  
Traffic Monitoring ◽  
Cloud Services ◽  
Future Research ◽  
Mesh Topology ◽  
Network Controller ◽  
Network Traffic Monitoring

Network traffic monitoring is vital for enhancing the overall network performance and for optimizing the traffic flows. However, an emerging growth of use in cloud services, internet-of-things, block-chain and data analytics, demand the hardware-based-network-controller to provide more features for expanding network architecture. Therefore, Software Defined Networking (SDN) offers a new solution in terms of scalability, usability and programmable software-based-network-controller for the legacy network infrastructure. In fact, SDN provides a dynamic platform for the network traffic monitoring using international standard. In this study, SDN setup and installation method uses a Mininet emulator containing a controller Ryu with switching hub component, OpenFlow switches, and nodes. The number of nodes is adding until reaches to 16 nodes and evaluated through different network scenarios (single, linear and tree topology). Findings show that the single topology gives a winning criterion compared to other topologies. SDN implementation is measured with performance parameters such as Throughput, Jitter, Bandwidth and Round-Trip Time between scenarios using the Ryu controller. Future research explores on the performance of SDN in larger network and investigates the efficiency and effectiveness of SDN implementation in mesh topology.

Sign in / Sign up

Export Citation Format

Share Document