Domain Name Detection and Classification Using Deep Neural Networks

  • B. BharathiEmail author
  • J. Bhuvana
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 969)


The malware families uses Domain Generated Algorithms (DGA) to generate and register different domains to connect to the command and Control server. To improve the automated analysis of DGA-based malware, we have developed an analysis system for detection and classification of DGA’s. In this paper we proposed to take a string of characters as input given in the domain names and classify them as either benign or malicious domain name using deep learning architectures such as Long Short Term Memory (LSTM) and Bidirectional LSTM. We have used the data set given by shared task on Detecting Malicious Domain names (DMD 2018). We have developed a system for both binary and multiclass classification task to detect the malicious domain names. We have observed that the proposed model for binary classification performed better than multiclass classification.


Domain Generated Algorithm Deep learning architecture LSTM Bidirectional LSTM 



We would like to thank the management of SSN College of Engineering for funding GPU system, which helps us to carry out the deep learning related research work.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of CSESSN College of EngineeringChennaiIndia

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