# A Survey on Honeypot Software and Data Analysis

arXiv: https://arxiv.org/abs/1608.06249 · HTML: https://mlsanity.com/paper/1608.06249/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2016.

## Built on by (most cited first, 7 of 7)

- [Chatbots in a Honeypot World](https://mlsanity.com/paper/2301.03771/) (2023)
- [OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models](https://mlsanity.com/paper/2510.01409/) (2025)
- [What are Attackers after on IoT Devices? An approach based on a multi-phased multi-faceted IoT honeypot ecosystem and data clustering](https://mlsanity.com/paper/2112.10974/) (2021)
- [Siren -- Advancing Cybersecurity through Deception and Adaptive Analysis](https://mlsanity.com/paper/2406.06225/) (2024)
- [Enabling Transparent Cyber Threat Intelligence Combining Large Language Models and Domain Ontologies](https://mlsanity.com/paper/2509.00081/) (2025)
- [Federated Temporal Attention Intelligence for Cyber-Resilient IoMT: Lightweight Digital Twins and PPO-Driven Honeypot Deception](https://mlsanity.com/paper/2606.21422/) (2026)
- [Privacy-Preserving Federated Temporal Graph Learning with Digital Twin--Guided Adaptive Deception for Cyber-Resilient IoMT](https://mlsanity.com/paper/2606.21513/) (2026)

## Abstract (arXiv)

In this survey, we give an extensive overview on honeypots. This includes not only honeypot software but also methodologies to analyse honeypot data.