Powered by OpenAIRE graph
Found an issue? Give us feedback
addClaim

Segugio: Efficient Behavior-Based Tracking of Malware-Control Domains in Large ISP Networks

Authors: Babak Rahbarinia; Roberto Perdisci; Manos Antonakakis;

Segugio: Efficient Behavior-Based Tracking of Malware-Control Domains in Large ISP Networks

Abstract

In this paper, we propose Segugio, a novel defense system that allows for efficiently tracking the occurrence of new malware-control domain names in very large ISP networks. Segugio passively monitors the DNS traffic to build a machine-domain bipartite graph representing who is querying what. After labelling nodes in this query behavior graph that are known to be either benign or malware-related, we propose a novel approach to accurately detect previously unknown malware-control domains. We implemented a proof-of-concept version of Segugio and deployed it in large ISP networks that serve millions of users. Our experimental results show that Segugio can track the occurrence of new malware-control domains with up to 94% true positives (TPs) at less than 0.1% false positives (FPs). In addition, we provide the following results: (1) we show that Segugio can also detect control domains related to new, previously unseen malware families, with 85% TPs at 0.1% FPs, (2) Segugio's detection models learned on traffic from a given ISP network can be deployed into a different ISP network and still achieve very high detection accuracy, (3) new malware-control domains can be detected days or even weeks before they appear in a large commercial domain name blacklist, and (4) we show that Segugio clearly outperforms Notos, a previously proposed domain name reputation system.

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    79
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 1%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
79
Top 10%
Top 1%
Top 10%
Upload OA version
Are you the author of this publication? Upload your Open Access version to Zenodo!
It’s fast and easy, just two clicks!