Since honeypots first appeared as an advanced network security concept they suffer from poor deployment and maintenance strategies. State-of-the-Art deployment is a manual process in which the honeypot needs to be configured and maintained by a network administrator. In this paper we present a method for a dynamic honeypot configuration, deployment and maintenance strategy based on machine learning techniques. Our method features an identification mechanism for machines and devices in a network. These entities are analysed and clustered. Based on the clusters, honeypots are intelligently deployed in the network. The proposed method needs no configuration and maintenance and is therefore a major advantage for the honeypot technology in modern network security.
@article{arxiv.2111.03884,
title = {An Adaptive Honeypot Configuration, Deployment and Maintenance Strategy},
author = {Daniel Fraunholz and Marc Zimmermann and Hans D. Schotten},
journal= {arXiv preprint arXiv:2111.03884},
year = {2021}
}