The Internet of Things (IoT) contributes to improving and automating the quality of our lives via devices and applications that progressively become more interconnected without user intervention in many areas such as smart homes, smart cities, smart transportation, and smart environment. However, IoT devices are vulnerable to cyberattacks. We cannot prevent all attacks, but they can be detected and resolved with the least damage. Moreover, they are connected for long periods of time without user intervention. Additionally, since they remain connected for long periods of time without user intervention, creative solutions must be devised to keep them safe, such as machine learning. The reach goal is to evaluate different machine learning algorithms to detect IoT network attacks quickly and effectively. The Bot-IoT dataset, which is derived from the original dataset, is used to evaluate various detection algorithms. Five different machine learning algorithms were tested on the two databases, and the results of the tests revealed high and accurate performance at all levels of the dataset.

中文翻译：

---

基于计算机视觉的物联网入侵检测系统

物联网 (IoT) 通过设备和应用程序改善和自动化我们的生活质量，这些设备和应用程序在智能家居、智能城市、智能交通和智能环境等许多领域无需用户干预即可逐渐变得更加互连。然而，物联网设备容易受到网络攻击。我们无法阻止所有攻击，但可以检测到并以最小的损害解决它们。此外，它们可以长时间连接而无需用户干预。此外，由于它们可以在没有用户干预的情况下长时间保持连接，因此必须设计创造性的解决方案来确保它们的安全，例如机器学习。Reach 的目标是评估不同的机器学习算法，以快速有效地检测物联网网络攻击。Bot-IoT 数据集，从原始数据集派生的，用于评估各种检测算法。在这两个数据库上测试了五种不同的机器学习算法，测试结果显示在数据集的所有级别上都具有高准确度的性能。