Clustering Malicious DNS Queries for Blacklist-Based Detection

Akihiro SATOH  Yutaka NAKAMURA  Daiki NOBAYASHI  Kazuto SASAI  Gen KITAGATA  Takeshi IKENAGA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E102-D   No.7   pp.1404-1407
Publication Date: 2019/07/01
Publicized: 2019/04/05
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8211
Type of Manuscript: LETTER
Category: Information Network
Keyword: 
malware,  blacklist,  DNS query,  machine learning,  

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Summary: 
Some of the most serious threats to network security involve malware. One common way to detect malware-infected machines in a network is by monitoring communications based on blacklists. However, such detection is problematic because (1) no blacklist is completely reliable, and (2) blacklists do not provide the sufficient evidence to allow administrators to determine the validity and accuracy of the detection results. In this paper, we propose a malicious DNS query clustering approach for blacklist-based detection. Unlike conventional classification, our cause-based classification can efficiently analyze malware communications, allowing infected machines in the network to be addressed swiftly.