Open AccessArtificial Neural Network Based DGA Botnet Detection
Author(s) -
Jiaxuan Wu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1578/1/012074
Subject(s) - botnet , computer science , computer security , domain name system , domain (mathematical analysis) , signature (topology) , network security , protocol (science) , artificial neural network , artificial intelligence , computer network , the internet , world wide web , medicine , mathematical analysis , geometry , mathematics , alternative medicine , pathology
In the recent years, botnet has become a serious threat to network security. As the result, the botnet detection solution is becoming an important topic for network security. DNS request is usually the first step to contact the C&C server of the bots controlled by the bot master and the detection of the DNS request domains is an effective way in detecting the bots. However, most botnets based on DNS protocol adopt Domain Generation Algorithm (DGA), which can change the domain randomly to hide themselves. Therefore, the traditional signature-based approach is rendered ineffective. Compared with the conventional ways of detection, the detection based on machine learning can obtain better detection result. In this work, we propose a botnet detection architecture based on Artificial Neural Network. We implement and evaluate the practicability of this solution with real datasets.