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：近十年来，技术创新应用与普及的快速发展日益增长。全球物联网系统的高速发展加剧了由恶意第三方带来的网络安全挑战。因此，兼顾安全考量与物联网系统局限性的可靠入侵检测与网络取证系统对于保护此类系统至关重要。物联网僵尸网络攻击已成为企业和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多种不同类型的攻击。与现有最先进的检测模型相比，所提模型在降低实施成本、加速训练与检测过程的同时，实现了更高的检测准确率。