International Journal of Circuits, Systems and Signal Processing

   
E-ISSN: 1998-4464
Volume 15, 2021

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of NAUN Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

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Volume 15, 2021

Title of the Paper: A Lightweight Hybrid Detection Method for Botnet

Authors: Wei Ma, Xing Wang, Jiguang Wang, Qianyun Chen

Pages: 960-969 

DOI: 10.46300/9106.2021.15.103     XML

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Abstract: Botnet is a serious threat for the Internet and it has created great damage to the Internet. How to detect botnet has become an ongoing endeavor research. Series of methods have been discussed in recent research. However, one of the remaining challenges is that the high computational overhead. In this paper, a lightweight hybrid botnet detection method is proposed. Considering the features in the botnet data packets and the characteristic of employing DGA (Domain Generation Algorithm) domain names to connect to the botnet, two sensors are designed and deployed individually and parallelly. Signature detection is used on the gateway sensor to dig out known bot software and deep learning based techniques are used on the DNS (Domain Name Server) server sensor to find DGA domain names. With this method, the computational overhead would be shared by the two sensors and experiments are conducted and the results indicate that the method is effective in detecting botnet