Autocorrelation Analysis of Financial Botnet Traffic
Publication Date
January 2018
Document Type
Contribution to a Book
Publication Title
Proceedings of the 4th International Conference on Information Systems Security and Privacy
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.
Recommended Citation
Prathiba Nagarajan, Fabio Troia, Thomas Austin, and Mark Stamp. "Autocorrelation Analysis of Financial Botnet Traffic" Proceedings of the 4th International Conference on Information Systems Security and Privacy (2018).
