Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation
Ying Xing, Hui Shu, Hao Zhao, Dannong Li, Li Guo
Abstract
With the continuous evolution of the Internet, as well as the development of the Internet of Things, smart terminals, cloud platforms, and social platforms, botnets showing the characteristics of platform diversification, communication concealment, and control intelligence. This survey analyzes and compares the most important efforts in the botnet detection area in recent years. It studies the mechanism characteristics of botnet architecture, life cycle, and command and control channel and provides a classification of botnet detection techniques. It focuses on the application of advanced technologies such as deep learning, complex network, swarm intelligence, moving target defense (MTD), and software-defined network (SDN) for botnet detection. From the four dimensions of service, intelligence, collaboration, and assistant, a common bot detection evaluation system (CBDES) is proposed, which defines a new global capability measurement standard. Combing with expert scores and objective weights, this survey proposes quantitative evaluation and gives a visual representation for typical detection methods. Finally, the challenges and future trends in the field of botnet detection are summarized.