Detail publikace

Botnet C&C Traffic and Flow Lifespans Using Survival Analysis

OUJEZSKÝ, V. HORVÁTH, T. ŠKORPIL, V.

Originální název

Botnet C&C Traffic and Flow Lifespans Using Survival Analysis

Anglický název

Botnet C&C Traffic and Flow Lifespans Using Survival Analysis

Jazyk

en

Originální abstrakt

This paper addresses the issue of detecting unwanted traffic in data networks, namely the detection of botnet networks. In this paper, we focused on a time behavioral analysis, more specifically said – lifespans of a simulated botnet network traffic, collected and discovered from NetFlow messages, and also of real botnet communication of a malware. As a method we chose survival analysis and for rigorous testing of differences Mantel–Cox test. Lifespans of those referred traffics are discovered and calculated by lifelines using Python language. Based on our research we have figured out a possibility to distinguish the individual lifespans of C&C communications that are identical to each other by using survival projection curves, although it occurred in a different time course.

Anglický abstrakt

This paper addresses the issue of detecting unwanted traffic in data networks, namely the detection of botnet networks. In this paper, we focused on a time behavioral analysis, more specifically said – lifespans of a simulated botnet network traffic, collected and discovered from NetFlow messages, and also of real botnet communication of a malware. As a method we chose survival analysis and for rigorous testing of differences Mantel–Cox test. Lifespans of those referred traffics are discovered and calculated by lifelines using Python language. Based on our research we have figured out a possibility to distinguish the individual lifespans of C&C communications that are identical to each other by using survival projection curves, although it occurred in a different time course.

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Dokumenty

BibTex


@article{BUT134474,
  author="Václav {Oujezský} and Tomáš {Horváth} and Vladislav {Škorpil}",
  title="Botnet C&C Traffic and Flow Lifespans Using Survival Analysis",
  annote="This paper addresses the issue of detecting unwanted traffic in data networks, namely the detection of botnet networks. In this paper, we focused on a time behavioral analysis, more specifically said – lifespans of a simulated botnet network traffic, collected and discovered from NetFlow messages, and also of real botnet communication of a malware. As a method we chose survival analysis and for rigorous testing of differences Mantel–Cox test. Lifespans of those referred traffics are discovered and calculated by lifelines using Python language.
Based on our research we have figured out a possibility to distinguish the individual lifespans of C&C communications that are identical to each other by using survival projection curves, although it occurred in a different time course.",
  address="International Science and Engineering Society, o.s.",
  chapter="134474",
  doi="10.11601/ijates.v6i1.205",
  howpublished="online",
  institution="International Science and Engineering Society, o.s.",
  number="1",
  volume="6",
  year="2017",
  month="march",
  pages="38--44",
  publisher="International Science and Engineering Society, o.s.",
  type="journal article - other"
}