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:为加强物联网网络的安全,减少恶意行为造成的破坏,提出了几种机器学习方法;然而,以高度准确和精确的方式侦查和分类袭击仍然是一个重大挑战;本文件提议建立一个网络攻击探测和网络交通分类系统,结合流机学习、深学习和综合学习技术;利用数据分析的多个阶段,该系统可以检测恶意交通流量的存在,并按其所代表的攻击类型进行分类;此外,我们展示了如何在IoT网络和从ML角度实施这一系统;该系统在三个IoT网络安全数据集中进行了评价,在该系统中,精确度和精确度均超过90%,并降低了错误警报率。</s>