The rapid progress in technology innovation usage and distribution has increased in the last decade. The rapid growth of the Internet of Things (IoT) systems worldwide has increased network security challenges created by malicious third parties. Thus, reliable intrusion detection and network forensics systems that consider security concerns and IoT systems limitations are essential to protect such systems. IoT botnet attacks are one of the significant threats to enterprises and individuals. Thus, this paper proposed an economic deep learning-based model for detecting IoT botnet attacks along with different types of attacks. The proposed model achieved higher accuracy than the state-of-the-art detection models using a smaller implementation budget and accelerating the training and detecting processes.

翻译：过去十年间，技术创新应用与传播的快速发展显著加速。全球物联网系统的迅猛增长，加剧了恶意第三方带来的网络安全挑战。因此，构建兼顾安全威胁与物联网系统局限性的可靠入侵检测与网络取证系统至关重要。物联网僵尸网络攻击已成为企业与个人面临的重大威胁之一。为此，本文提出了一种基于经济型深度学习的模型，用于检测物联网僵尸网络攻击及其他不同类型攻击。与现有最优检测模型相比，所提模型在降低部署成本、加速训练与检测过程的同时，实现了更高的检测精度。