Autocorrelation Analysis of Financial Botnet Traffic

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

2018

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.

Paper Citation

in Harvard Style

Nagarajan P., Di Troia F., Austin T. and Stamp M. (2018). Autocorrelation Analysis of Financial Botnet Traffic.In Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ForSE, ISBN 978-989-758-282-0, pages 599-606. DOI: 10.5220/0006685705990606

in Bibtex Style

@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 - Volume 1: ForSE,},
year={2018},
pages={599-606},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006685705990606},
isbn={978-989-758-282-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Information Systems Security and Privacy - Volume 1: ForSE,
TI - Autocorrelation Analysis of Financial Botnet Traffic
SN - 978-989-758-282-0
AU - Nagarajan P.
AU - Di Troia F.
AU - Austin T.
AU - Stamp M.
PY - 2018
SP - 599
EP - 606
DO - 10.5220/0006685705990606