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，技术创新的快速进步使得技术的使用和分布得以增加。全球物联网（IoT）系统的快速增长带来了恶意第三方产生的网络安全挑战。因此，考虑安全问题和物联网系统的限制，可靠的入侵检测和网络取证系统对于保护这种类型的系统至关重要。 IoT botnet攻击是企业和个人面临的主要威胁之一。因此，本文提出了一种基于经济的深度学习模型来检测IoT Botnet攻击及其不同类型的攻击。所提出的模型使用更小的实现预算，在训练和检测过程中加速，其准确度高于现有检测模型。