A Topology Based Flow Model for Computing Domain Reputation

The Domain Name System (DNS) is an essential component of the internet infrastructure that translates domain names into IP addresses. Recent incidents verify the enormous damage of malicious activities utilizing DNS such as bots that use DNS to locate their command&control servers. Detecting mal...

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Bibliographic Details
Published inData and Applications Security and Privacy XXIX Vol. 9149; pp. 277 - 292
Main Authors Mishsky, Igor, Gal-Oz, Nurit, Gudes, Ehud
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2015
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Online AccessGet full text
ISBN3319208098
9783319208091
ISSN0302-9743
1611-3349
1611-3349
DOI10.1007/978-3-319-20810-7_20

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Summary:The Domain Name System (DNS) is an essential component of the internet infrastructure that translates domain names into IP addresses. Recent incidents verify the enormous damage of malicious activities utilizing DNS such as bots that use DNS to locate their command&control servers. Detecting malicious domains using the DNS network is therefore a key challenge. We project the famous expression Tell me who your friends are and I will tell you who you are, motivating many social trust models, on the internet domains world. A domain that is related to malicious domains is more likely to be malicious as well. In this paper, our goal is to assign reputation values to domains and IPs indicating the extent to which we consider them malicious. We start with a list of domains known to be malicious or benign and assign them reputation scores accordingly. We then construct a DNS based graph in which nodes represent domains and IPs. Our new approach for computing domain reputation applies a flow algorithm on the DNS graph to obtain the reputation of domains and identify potentially malicious ones. The experimental evaluation of the flow algorithm demonstrates its success in predicting malicious domains.
ISBN:3319208098
9783319208091
ISSN:0302-9743
1611-3349
1611-3349
DOI:10.1007/978-3-319-20810-7_20