| Title | Botnet Detection Based on Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Yang, Xiaoran, Guo, Zhen, Mai, Zetian |
| Conference Name | 2022 International Conference on Blockchain Technology and Information Security (ICBCTIS) |
| Date Published | jul |
| Keywords | Botnet, botnets security, composability, compositionality, Deep Learning, machine learning botnet detection cypher security Network traffic detection, Metrics, Neural networks, Production, pubcrawl, reinforcement learning, resilience, Resiliency, telecommunication traffic, Time series analysis |
| Abstract | A botnet is a new type of attack method developed and integrated on the basis of traditional malicious code such as network worms and backdoor tools, and it is extremely threatening. This course combines deep learning and neural network methods in machine learning methods to detect and classify the existence of botnets. This sample does not rely on any prior features, the final multi-class classification accuracy rate is higher than 98.7%, the effect is significant. |
| DOI | 10.1109/ICBCTIS55569.2022.00056 |
| Citation Key | yang_botnet_2022 |
Botnet Detection Based on Machine Learning