An Adaptive Honeypot Configuration, Deployment and Maintenance Strategy

TL;DR

This paper introduces a machine learning-based adaptive strategy for automatically configuring, deploying, and maintaining honeypots in networks, reducing manual effort and improving security effectiveness.

Contribution

It presents a novel dynamic honeypot management method that requires no manual configuration or maintenance, enhancing modern network security practices.

Findings

Automated honeypot deployment based on network entity clustering

Reduction in manual configuration and maintenance efforts

Improved adaptability of honeypots in dynamic network environments

Abstract

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.