BotCatcher:botnet detection system based on deep learning |
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Authors: | Di WU Binxing FANG Xiang CUI Qixu LIU |
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Affiliation: | 1. Institute of Information Engineering,Chinese Academy of Sciences,Beijing 100093,China;2. School of Cyber Security,University of Chinese Academy of Sciences,Beijing 100049,China;3. Cyberspace Institute of Advanced Technology,Guangzhou University,Guangzhou 510006,China;4. Institute of Electronic and Information Engineering of UESTC in Guangdong,Dongguan 523808,China;5. School of Cyberspace Security,Beijing University of Posts and Telecommunications,Beijing 100876,China |
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Abstract: | Machine learning technology has wide application in botnet detection.However,with the changes of the forms and command and control mechanisms of botnets,selecting features manually becomes increasingly difficult.To solve this problem,a botnet detection system called BotCatcher based on deep learning was proposed.It automatically extracted features from time and space dimension,and established classifier through multiple neural network constructions.BotCatcher does not depend on any prior knowledge which about the protocol and the topology,and works without manually selecting features.The experimental results show that the proposed model has good performance in botnet detection and has ability to accurately identify botnet traffic . |
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Keywords: | botnet deep learning detection feature |
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