A fully scalable big data framework for Botnet detection based on network traffic analysis

2020 ◽  
Vol 512 ◽  
pp. 629-640 ◽  
Author(s):  
S.H. Mousavi ◽  
M. Khansari ◽  
R. Rahmani
2021 ◽  
Author(s):  
Kovtsur Maxim ◽  
Kistruga Anton ◽  
Mikhailova Anastasiya ◽  
Potemkin Pavel ◽  
Volkogonov Vladimir

Author(s):  
Mahesh Pawar ◽  
Anjana Panday ◽  
Ratish Agrawal ◽  
Sachin Goyal

Network is a connection of devices in either a wired or wireless manner. Networking has become a part and parcel of computing in the present world. They form the backbone of the modern-day computing business. Hence, it is important for networks to remain alive, up, and reliable all the time. A way to ensure that is network traffic analysis. Network traffic analysis mainly deals with a study of bandwidth utilization, transmission and reception rates, error rates, etc., which is important to keep the network smooth and improve economic efficiency. The proposed model approaches network traffic analysis in a way to collect network information and then deal with it using technologies available for big data analysis. The model aims to analyze the collected information to calculate a factor called reliability factor, which can guide in effective network management. The model also aims to assist the network administrator by informing him whether network traffic is high or low, and the administrator can then take targeted steps to prevent network failure.


2020 ◽  
Author(s):  
Sumit Kumari ◽  
Neetu Sharma ◽  
Prashant Ahlawat

Sign in / Sign up

Export Citation Format

Share Document