DomainChroma: Building actionable threat intelligence from malicious domain names. Issue 77 (August 2018)

• Record Type: Journal Article
• Authors: Chiba, Daiki; Akiyama, Mitsuaki; Yagi, Takeshi; Hato, Kunio; Mori, Tatsuya; Goto, Shigeki
• Is Part Of: Computers & security. Issue 77(2018)
• Page Start: 138
• Page End: 161
• Publication Date: 2018-08
• Subjects: Malicious domain name -- Categorization -- Actionable threat intelligence -- Defense point -- Domain blacklist -- Abuse report; Computer security -- Periodicals; Electronic data processing departments -- Security measures -- Periodicals; 005.805
• DOI: 10.1016/j.cose.2018.03.013
• Languages: English
• ISSNs: 0167-4048

Abstract

Highlights: A system for deciding best defense solution for a malicious domain name is proposed. The system relies on a new concept of malicious domain name chromatography. The system can generate effective domain name blacklists with no collateral damage. Abstract: Since the 1980s, domain names and the domain name system (DNS) have been used and abused. Although legitimate Internet users rely on domain names as indispensable infrastructures for using the Internet, attackers use or abuse them as reliable, instantaneous, and distributed attack infrastructures. However, there is a lack of complete understanding of such domain-name abuses and methods for coping with them. In this study, we designed and implemented a unified analysis system combining current defense solutions to build actionable threat intelligence from malicious domain names. The basic concept underlying our system is malicious domain name chromatography . Our analysis system can distinguish among mixtures of malicious domain names for websites. On the basis of this concept, we do not create a hodgepodge of current solutions but design separation of abused domain names and offer actionable threat intelligence or defense information by considering the characteristics of malicious domain names as well as the possible defense solutions and points of defense. Finally, we evaluated our analysis system and defense-information output using a large real dataset to show the effectiveness and validity of our system.