Internet of things (IoT) devices and applications are dramatically increasing worldwide, resulting in more cybersecurity challenges. Among these challenges are malicious activities that target IoT devices and cause serious damage, such as data leakage, phishing and spamming campaigns, distributed denial-of-service (DDoS) attacks, and security breaches. In this paper, a stacked deep learning method is proposed to detect malicious traffic data, particularly malicious attacks targeting IoT devices. The proposed stacked deep learning method is bundled with five pretrained residual networks (ResNets) to deeply learn the characteristics of the suspicious activities and distinguish them from normal traffic. Each pretrained ResNet model consists of 10 residual blocks. We used two large datasets to evaluate the performance of our detection method. We investigated two heterogeneous IoT environments to make our approach deployable in any IoT setting. Our proposed method has the ability to distinguish between benign and malicious traffic data and detect most IoT attacks. The experimental results show that our proposed stacked deep learning method can provide a higher detection rate in real time compared with existing classification techniques.

中文翻译：

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物联网网络攻击检测的堆叠式深度学习方法

物联网（IoT）设备和应用程序在世界范围内正在急剧增长，从而带来了更多的网络安全挑战。这些挑战包括针对物联网设备并造成严重破坏的恶意活动，例如数据泄漏，网络钓鱼和垃圾邮件活动，分布式拒绝服务（DDoS）攻击和安全漏洞。本文提出了一种堆栈式深度学习方法来检测恶意流量数据，尤其是针对IoT设备的恶意攻击。所提出的堆叠式深度学习方法与五个预训练的残差网络（ResNets）捆绑在一起，可深入学习可疑活动的特征并将其与正常流量区分开。每个预先训练的ResNet模型都包含10个残差块。我们使用了两个大型数据集来评估我们检测方法的性能。我们调查了两个异构的物联网环境，以使我们的方法可在任何物联网环境中部署。我们提出的方法具有区分良性和恶意流量数据并检测大多数IoT攻击的能力。实验结果表明，与现有分类技术相比，本文提出的堆叠式深度学习方法可以提供更高的实时检测率。