  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.
