Several Machine Learning (ML) methodologies have been proposed to improve security in Internet Of Things (IoT) networks and reduce the damage caused by the action of malicious agents. However, detecting and classifying attacks with high accuracy and precision is still a major challenge. This paper proposes an online attack detection and network traffic classification system, which combines stream Machine Learning, Deep Learning, and Ensemble Learning technique. Using multiple stages of data analysis, the system can detect the presence of malicious traffic flows and classify them according to the type of attack they represent. Furthermore, we show how to implement this system both in an IoT network and from an ML point of view. The system was evaluated in three IoT network security datasets, in which it obtained accuracy and precision above 90% with a reduced false alarm rate.

翻译：多种机器学习方法已被提出，用于提升物联网网络的安全性并降低恶意代理行为造成的损害。然而，以高准确率和精确度检测并分类攻击仍是一项重大挑战。本文提出了一种在线攻击检测与网络流量分类系统，该技术融合了流式机器学习、深度学习和集成学习方法。通过多阶段数据分析，该系统能够检测恶意流量流的存在，并根据其代表的攻击类型对其进行分类。此外，我们展示了如何在物联网网络以及机器学习视角下实现该系统。该系统在三个物联网网络安全数据集上进行了评估，获得了超过90%的准确率和精确度，且误报率较低。