Related papers: Design and implementation of intelligent packet filtering in IoT microcontroller-based devices

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

URL: http://arxiv.org/abs/2305.19214v1
Date: Tue, 30 May 2023 17:03:36 GMT
Title: Design and implementation of intelligent packet filtering in IoT microcontroller-based devices
Authors: Gustavo de Carvalho Bertoli, Gabriel Victor C. Fernandes, Pedro H. Borges Monici, C\'esar H. de Araujo Guibo, Louren\c{c}o Alves Pereira Jr., Aldri Santos
Abstract summary: Internet of Things (IoT) devices are increasingly pervasive and essential components in enabling new applications and services. Ensuring robust cybersecurity measures is essential to protect IoT devices from malicious attacks. We introduce T800, a low-resource packet filter that utilizes machine learning (ML) algorithms to classify packets in IoT devices.
Score: 1.4500636542366327
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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