Autocorrelation Analysis of Financial Botnet Traffic

Prathiba Nagarajan; Fabio Di Troia; Thomas H. Austin; Mark Stamp

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Paper

Autocorrelation Analysis of Financial Botnet Traffic

In Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ForSE, 599-606, 2018 , Funchal, Madeira, Portugal

Authors: Prathiba Nagarajan ; Fabio Di Troia ; Thomas H. Austin and Mark Stamp

Affiliation: San Jose State University, United States

Keyword(s): Botnet, Autocorrelation, Periodicity, Citadel, SpyEye, Zeus, Tinba.

Abstract: A botnet consists of a network of infected computers that can be controlled remotely via a command and control (C&C) server. Typically, a botnet requires frequent communication between a C&C server and the infected nodes. Previous approaches to detecting botnets have included various machine learning techniques based on features extracted from network traffic. In this research, we conduct autocorrelation analysis of traffic generated by financial botnets, and we show that periodicity is a highly distinguishing feature for detecting such botnets.

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Paper citation in several formats:

Nagarajan, P., Di Troia, F., Austin, T. H. and Stamp, M. (2018). Autocorrelation Analysis of Financial Botnet Traffic. In Proceedings of the 4th International Conference on Information Systems Security and Privacy - ForSE; ISBN 978-989-758-282-0; ISSN 2184-4356, SciTePress, pages 599-606. DOI: 10.5220/0006685705990606

@conference{forse18,
author={Prathiba Nagarajan and Fabio {Di Troia} and Thomas H. Austin and Mark Stamp},
title={Autocorrelation Analysis of Financial Botnet Traffic},
booktitle={Proceedings of the 4th International Conference on Information Systems Security and Privacy - ForSE},
year={2018},
pages={599-606},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006685705990606},
isbn={978-989-758-282-0},
issn={2184-4356},
}

TY - CONF

JO - Proceedings of the 4th International Conference on Information Systems Security and Privacy - ForSE
TI - Autocorrelation Analysis of Financial Botnet Traffic
SN - 978-989-758-282-0
IS - 2184-4356
AU - Nagarajan, P.
AU - Di Troia, F.
AU - Austin, T.
AU - Stamp, M.
PY - 2018
SP - 599
EP - 606
DO - 10.5220/0006685705990606
PB - SciTePress