| Title | A Mechine Learning Approach for Botnet Detection Using LightGBM |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Gong, Dehao, Liu, Yunqing |
| Conference Name | 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA) |
| Keywords | Botnet, Botnet detection, botnets security, Classification algorithms, Complexity theory, component, composability, compositionality, Forestry, machine learning, Metrics, pubcrawl, Radio frequency, resilience, Resiliency, security, Systems architecture, Traffic analysis |
| Abstract | The botnet-based network assault are one of the most serious security threats overlay the Internet this day. Although significant progress has been made in this region of research in recent years, it is still an ongoing and challenging topic to virtually direction the threat of botnets due to their continuous evolution, increasing complexity and stealth, and the difficulties in detection and defense caused by the limitations of network and system architectures. In this paper, we propose a novel and efficient botnet detection method, and the results of the detection method are validated with the CTU-13 dataset. |
| DOI | 10.1109/CVIDLICCEA56201.2022.9824033 |
| Citation Key | gong_mechine_2022 |
A Mechine Learning Approach for Botnet Detection Using LightGBM