Presentation
Autocorrelation Analysis of Financial Botnet Traffic
2nd International Workshop on Formal Methods for Security Engineering (ForSE 2018)
(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.
Disciplines
Publication Date
January 22, 2018
Location
Funchal, Madeira, Portugal
Citation Information
Prathiba Nagarajan, Fabio Di. Troia, Thomas H. Austin and Mark Stamp. "Autocorrelation Analysis of Financial Botnet Traffic" 2nd International Workshop on Formal Methods for Security Engineering (ForSE 2018) (2018) Available at: http://works.bepress.com/thomas_austin/29/