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.

翻译：物联网（IoT）设备日益普及，已成为支持新型应用与服务的关键组件。然而，其广泛应用也使其暴露于可利用的漏洞与缺陷中，可能导致重大损失。在此背景下，确保稳健的网络安全措施对保护物联网设备免受恶意攻击至关重要。然而，当前能够为物联网设备提供灵活策略规范和更高安全级别的解决方案十分稀缺。为填补这一空白，我们提出T800——一种利用机器学习（ML）算法对物联网设备数据包进行分类的低资源包过滤器。我们构建了详细的性能基准测试框架，并在ESP32系统级芯片微控制器及ESP-IDF框架上验证了T800的有效性。评估结果表明，T800是一种高效的解决方案，通过将未经请求的恶意流量排除在处理流水线之外，提升了设备的计算能力。此外，T800可适应不同系统，并为基于ESP32的物联网系统中基于机器学习的安防机制提供了文档完善的性能评估策略。本研究有助于提升资源受限物联网设备的网络安全水平，并提供了一种可扩展、高效的解决方案，用于增强物联网系统的安全性。