Detection of Exfiltration and Tunneling over DNS

被引:19
|
作者
Das, Anirban [1 ]
Shen, Min-Yi [2 ]
Shashanka, Madhu [3 ]
Wang, Jisheng [2 ]
机构
[1] Samsung Res Amer Inc, Palo Alto, CA USA
[2] Hewlett Packard Enterprise, Palo Alto, CA USA
[3] Charles Schwab, San Francisco, CA USA
关键词
DNS; C&C; exfiltration; machine learning; indicator of compromise; IOC; DNS tunnel;
D O I
10.1109/ICMLA.2017.00-71
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes a method to detect two primary means of using the Domain Name System (DNS) for malicious purposes. We develop machine learning models to detect information exfiltration from compromised machines and the establishment of command & control (C&C) servers via tunneling. We validate our approach by experiments where we successfully detect a malware used in several recent Advanced Persistent Threat (APT) attacks [1]. The novelty of our method is its robustness, simplicity, scalability, and ease of deployment in a production environment.
引用
收藏
页码:737 / 742
页数:6
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