  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.
