Visible to the public Overview of Botnet Detection Based on Machine Learning

TitleOverview of Botnet Detection Based on Machine Learning
Publication TypeConference Paper
Year of Publication2018
AuthorsDong, X., Hu, J., Cui, Y.
Conference Name2018 3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE)
KeywordsBotnet, Botnet detection, botnets, Communication networks, complex network environment, compositionality, Data models, feature extraction, information industry, invasive software, learning (artificial intelligence), machine learning, Metrics, network equipment, Network security, network security problems, network space security, protection detection, pubcrawl, resilience, Resiliency, security, security industry, Servers

With the rapid development of the information industry, the applications of Internet of things, cloud computing and artificial intelligence have greatly affected people's life, and the network equipment has increased with a blowout type. At the same time, more complex network environment has also led to a more serious network security problem. The traditional security solution becomes inefficient in the new situation. Therefore, it is an important task for the security industry to seek technical progress and improve the protection detection and protection ability of the security industry. Botnets have been one of the most important issues in many network security problems, especially in the last one or two years, and China has become one of the most endangered countries by botnets, thus the huge impact of botnets in the world has caused its detection problems to reset people's attention. This paper, based on the topic of botnet detection, focuses on the latest research achievements of botnet detection based on machine learning technology. Firstly, it expounds the application process of machine learning technology in the research of network space security, introduces the structure characteristics of botnet, and then introduces the machine learning in botnet detection. The security features of these solutions and the commonly used machine learning algorithms are emphatically analyzed and summarized. Finally, it summarizes the existing problems in the existing solutions, and the future development direction and challenges of machine learning technology in the research of network space security.

Citation Keydong_overview_2018