An Adaptive Honeypot Configuration, Deployment and Maintenance Strategy
This addresses the inefficiency of honeypot deployment for network administrators, though it appears incremental as it builds on existing honeypot concepts with automation.
The paper tackles the problem of manual honeypot configuration and maintenance in network security by proposing a dynamic strategy using machine learning to automatically identify, cluster, and deploy honeypots, eliminating the need for manual intervention.
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.