U-PoT: A Honeypot Framework for UPnP-Based IoT Devices
Not on the map. Why?
Abstract (arXiv)
The ubiquitous nature of the IoT devices has brought serious security implications to its users. A lot of consumer IoT devices have little to no security implementation at all, thus risking user's privacy and making them target of mass cyber-attacks. Indeed, recent outbreak of Mirai botnet and its variants have already proved the lack of security on the IoT world. Hence, it is important to understand the security issues and attack vectors in the IoT domain. Though significant research has been done to secure traditional computing systems, little focus was given to the IoT realm. In this work, we reduce this gap by developing a honeypot framework for IoT devices. Specifically, we introduce U-PoT: a novel honeypot framework for capturing attacks on IoT devices that use Universal Plug and Play (UPnP) protocol. A myriad of smart home devices including smart switches, smart bulbs, surveillance cameras, smart hubs, etc. uses the UPnP protocol. Indeed, a simple search on Shodan IoT search engine lists 1,676,591 UPnP devices that are exposed to public network. The popularity and ubiquitous nature of UPnP-based IoT device necessitates a full-fledged IoT honeypot system for UPnP devices. Our novel framework automatically creates a honeypot from UPnP device description documents and is extendable to any device types or vendors that use UPnP for communication. To the best of our knowledge, this is the first work towards a flexible and configurable honeypot framework for UPnP-based IoT devices. We released U-PoT under an open source license for further research and created a database of UPnP device descriptions. We also evaluated our framework on two emulated deices. Our experiments show that the emulated devices are able to mimic the behavior of a real IoT device and trick vendor-provided device management applications or popular IoT search engines while having minimal performance ovherhead.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Dec 2018.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 0; 2023: 2; 2024: 1.
4 papers.
- 2024LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systemscited by 10
- 2023HoneyIoT: Adaptive High-Interaction Honeypot for IoT Devices Through Reinforcement Learningcited by 2
- 2023AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devicescited by 1
- 2021What are Attackers after on IoT Devices? An approach based on a multi-phased multi-faceted IoT honeypot ecosystem and data clusteringcited by 0
- 2024LLM Honeypot: Leveraging Large Language Models as Advanced Interactive Honeypot Systems12 Sep 2024
- 2023HoneyIoT: Adaptive High-Interaction Honeypot for IoT Devices Through Reinforcement Learning10 May 2023
- 2023AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices22 Mar 2023
- 2021What are Attackers after on IoT Devices? An approach based on a multi-phased multi-faceted IoT honeypot ecosystem and data clustering21 Dec 2021