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DNS Tunneling Detection Using Feedforward Neural Network

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  • Yakov Bubnov

    (Belarusian State University of Informatics and Radioelectronics)

Abstract

This paper addresses a problem of detecting Domain Name System (DNS) tunneling in a computer network. Unauthorized data transfer exploits DNS tunneling technique to conceal network activity in a regular DNS traffic. Contemporary intrusion prevention equipment does not provide reasonable protection from sensitive information stealing. Given the DNS queries from both legitimate and adversary clients this paper proposes a machine-learning method of distinguishing tunneling strategies. More precisely, it describes a multi-label model of feedforward neural network that classifies some of well-known tunneling strategies counting legitimate traffic. The paper contains analysis of classification quality and accuracy of the developed model.

Suggested Citation

  • Yakov Bubnov, 2018. "DNS Tunneling Detection Using Feedforward Neural Network," European Journal of Engineering and Technology Research, European Open Science, vol. 3(11), pages 16-19, October.
    • Handle: RePEc:epw:ejeng0:v:3:y:2018:i:11:id:60963
    DOI: 10.24018/ejeng.2018.3.11.963
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