# AIIPot: Adaptive Intelligent-Interaction Honeypot for IoT Devices

arXiv: https://arxiv.org/abs/2303.12367 · HTML: https://mlsanity.com/paper/2303.12367/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 14 references that are in the map.
- First posted on arXiv 22 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [U-PoT: A Honeypot Framework for UPnP-Based IoT Devices](https://mlsanity.com/paper/1812.05558/) (2018)
- [SIPHON: Towards Scalable High-Interaction Physical Honeypots](https://mlsanity.com/paper/1701.02446/) (2017)
- [ThingPot: an interactive Internet-of-Things honeypot](https://mlsanity.com/paper/1807.04114/) (2018)
- [Game-Theoretic and Machine Learning-based Approaches for Defensive Deception: A Survey](https://mlsanity.com/paper/2101.10121/) (2021)

## Built on by (most cited first, 1 of 1)

- [Cyber Deception: State of the art, Trends and Open challenges](https://mlsanity.com/paper/2409.07194/) (2024)

## Abstract (arXiv)

The proliferation of the Internet of Things (IoT) has raised concerns about the security of connected devices. There is a need to develop suitable and cost-efficient methods to identify vulnerabilities in IoT devices in order to address them before attackers seize opportunities to compromise them. The deception technique is a prominent approach to improving the security posture of IoT systems. Honeypot is a popular deception technique that mimics interaction in real fashion and encourages unauthorised users (attackers) to launch attacks. Due to the large number and the heterogeneity of IoT devices, manually crafting the low and high-interaction honeypots is not affordable. This has forced researchers to seek innovative ways to build honeypots for IoT devices. In this paper, we propose a honeypot for IoT devices that uses machine learning techniques to learn and interact with attackers automatically. The evaluation of the proposed model indicates that our system can improve the session length with attackers and capture more attacks on the IoT network.