IoT Botnet Detection Using an Economic Deep Learning Model
This addresses network security challenges for IoT systems, offering a cost-effective solution, though it appears incremental as it builds on existing deep learning methods.
The paper tackled IoT botnet detection by proposing an economic deep learning model that achieved higher accuracy than state-of-the-art models while using a smaller budget and faster training/detection processes.
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.