SIPHON: Towards Scalable High-Interaction Physical Honeypots
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Abstract (arXiv)
In recent years, the emerging Internet-of-Things (IoT) has led to rising concerns about the security of networked embedded devices. In this work, we focus on the adaptation of Honeypots for improving the security of IoTs. Low-interaction honeypots are used so far in the context of IoT. Such honeypots are limited and easily detectable, and thus, there is a need to find ways how to develop high-interaction, reliable, IoT honeypots that will attract skilled attackers. In this work, we propose the SIPHON architecture - a Scalable high-Interaction Honeypot platform for IoT devices. Our architecture leverages IoT devices that are physically at one location and are connected to the Internet through so-called wormholes distributed around the world. The resulting architecture allows exposing few physical devices over a large number of geographically distributed IP addresses. We demonstrate the proposed architecture in a large scale experiment with 39 wormhole instances in 16 cities in 9 countries. Based on this setup, six physical IP cameras, one NVR and one IP printer are presented as 85 real IoT devices on the Internet, attracting a daily traffic of 700MB for a period of two months. A preliminary analysis of the collected traffic indicates that devices in some cities attracted significantly more traffic than others (ranging from 600 000 incoming TCP connections for the most popular destination to less than 50000 for the least popular). We recorded over 400 brute-force login attempts to the web-interface of our devices using a total of 1826 distinct credentials, from which 11 attempts were successful. Moreover, we noted login attempts to Telnet and SSH ports some of which used credentials found in the recently disclosed Mirai malware.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jan 2017.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 2.
3 papers.
- 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
- 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