# Design and implementation of intelligent packet filtering in IoT microcontroller-based devices

arXiv: https://arxiv.org/abs/2305.19214 · HTML: https://mlsanity.com/paper/2305.19214/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 35 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 7 of 7)

- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Technical Aspects of Cyber Kill Chain](https://mlsanity.com/paper/1606.03184/) (2016)
- [Scanning the Internet for ROS: A View of Security in Robotics Research](https://mlsanity.com/paper/1808.03322/) (2018)
- [Generalizing intrusion detection for heterogeneous networks: A stacked-unsupervised federated learning approach](https://mlsanity.com/paper/2209.00721/) (2022)
- [Network Intrusion Detection System in a Light Bulb](https://mlsanity.com/paper/2210.03254/) (2022)
- [Efficient Intrusion Detection on Low-Performance Industrial IoT Edge Node Devices](https://mlsanity.com/paper/1908.03964/) (2019)
- [Missed Opportunities: Measuring the Untapped TLS Support in the Industrial Internet of Things](https://mlsanity.com/paper/2206.00322/) (2022)

## Abstract (arXiv)

Internet of Things (IoT) devices are increasingly pervasive and essential components in enabling new applications and services. However, their widespread use also exposes them to exploitable vulnerabilities and flaws that can lead to significant losses. In this context, ensuring robust cybersecurity measures is essential to protect IoT devices from malicious attacks. However, the current solutions that provide flexible policy specifications and higher security levels for IoT devices are scarce. To address this gap, we introduce T800, a low-resource packet filter that utilizes machine learning (ML) algorithms to classify packets in IoT devices. We present a detailed performance benchmarking framework and demonstrate T800's effectiveness on the ESP32 system-on-chip microcontroller and ESP-IDF framework. Our evaluation shows that T800 is an efficient solution that increases device computational capacity by excluding unsolicited malicious traffic from the processing pipeline. Additionally, T800 is adaptable to different systems and provides a well-documented performance evaluation strategy for security ML-based mechanisms on ESP32-based IoT systems. Our research contributes to improving the cybersecurity of resource-constrained IoT devices and provides a scalable, efficient solution that can be used to enhance the security of IoT systems.