Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation

Xing Ying; Shu Hui; Zhao HaoLi DannongGuo Li

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Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation

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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.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2021年第17期|6640499.1-6640499.24|共24页
作者
Xing Ying; Shu Hui; Zhao HaoLi DannongGuo Li;
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Univ Informat & Engn, State Key Lab Math Engn & Adv Comp, Zhengzhou 450000, Peoples R China|Zhongyuan Univ Technol, Software Coll, Zhengzhou 450000, Peoples R China;

Univ Informat & Engn, State Key Lab Math Engn & Adv Comp, Zhengzhou 450000, Peoples R China;

PLA Strateg Support Force Informat Engn Univ, Teaching & Res Support Ctr, Zhengzhou 450000, Peoples R ChinaZhongyuan Univ Technol, Software Coll, Zhengzhou 450000, Peoples R China;

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Survey on Botnet Detection Techniques: Classification, Methods, and Evaluation

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