For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
Clustering Malicious DNS Queries for Blacklist-Based Detection
Akihiro SATOH Yutaka NAKAMURA Daiki NOBAYASHI Kazuto SASAI Gen KITAGATA Takeshi IKENAGA
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2019/07/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Information Network
malware, blacklist, DNS query, machine learning,
Full Text: PDF>>
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.