# SunBlock: Cloudless Protection for IoT Systems

arXiv: https://arxiv.org/abs/2401.14332 · HTML: https://mlsanity.com/paper/2401.14332/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 25 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Machine learning for Internet of Things data analysis: A survey](https://mlsanity.com/paper/1802.06305/) (2018)
- [IoT Device Identification Using Deep Learning](https://mlsanity.com/paper/2002.11686/) (2020)
- [The Case for Retraining of ML Models for IoT Device Identification at the Edge](https://mlsanity.com/paper/2011.08605/) (2020)
- [Revisiting IoT Device Identification](https://mlsanity.com/paper/2107.07818/) (2021)
- [Blocking without Breaking: Identification and Mitigation of Non-Essential IoT Traffic](https://mlsanity.com/paper/2105.05162/) (2021)
- [Protected or Porous: A Comparative Analysis of Threat Detection Capability of IoT Safeguards](https://mlsanity.com/paper/2304.03045/) (2023)
- [Rapid IoT Device Identification at the Edge](https://mlsanity.com/paper/2110.13941/) (2021)
- [Feature-Sniffer: Enabling IoT Forensics in OpenWrt based Wi-Fi Access Points](https://mlsanity.com/paper/2302.06991/) (2023)

## Built on by (most cited first, 2 of 2)

- [Intelligent Detection of Non-Essential IoT Traffic on the Home Gateway](https://mlsanity.com/paper/2504.18571/) (2025)
- [Generalizable IoT Traffic Representations for Cross-Network Device Identification](https://mlsanity.com/paper/2601.19315/) (2026)

## Abstract (arXiv)

With an increasing number of Internet of Things (IoT) devices present in homes, there is a rise in the number of potential information leakage channels and their associated security threats and privacy risks. Despite a long history of attacks on IoT devices in unprotected home networks, the problem of accurate, rapid detection and prevention of such attacks remains open. Many existing IoT protection solutions are cloud-based, sometimes ineffective, and might share consumer data with unknown third parties. This paper investigates the potential for effective IoT threat detection locally, on a home router, using AI tools combined with classic rule-based traffic-filtering algorithms. Our results show that with a slight rise of router hardware resources caused by machine learning and traffic filtering logic, a typical home router instrumented with our solution is able to effectively detect risks and protect a typical home IoT network, equaling or outperforming existing popular solutions, without any effects on benign IoT functionality, and without relying on cloud services and third parties.