Detection of DoH Tunnels using Time-series Classification of Encrypted Traffic

被引:100
|
作者
MontazeriShatoori, Mohammadreza [1 ]
Davidson, Logan [1 ]
Kaur, Gurdip [1 ]
Lashkari, Arash Habibi [1 ]
机构
[1] Univ New Brunswick, Canadian Inst Cybersecur, Fredericton, NB, Canada
关键词
DNS over HTTPS; DoH; encrypted traffic characterization; malicious DoH; machine learning; deep learning;
D O I
10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00026
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Computer networks have fallen easy prey to cyber attacks in the ever-evolving internet services. Domain Name System (DNS) has also not remained untouched with these cybercrime attempts. Encrypted HyperText Transfer Protocol (HTTP) traffic over Secure Socket Layer (SSL), alternatively called HTTPS, has succeeded to prevent DNS attacks to a great extent. To secure DNS traffic, the security community has introduced the concept of DNS over HTTPS (DoH) to improve user privacy and security by combating eavesdropping and DNS data manipulation on the way to prevent Man-inthe-Middle (MitM) attacks. This paper discusses one of the persistent security concerns, abuse of DNS protocol to create covert channels by tunneling data through DNS packets. We identify tunneling activities that utilize DNS communications over HTTPS by presenting a two-layered approach to detect and characterize DoH traffic using time-series classifiers.
引用
收藏
页码:63 / 70
页数:8
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