IoT Botnet Detection Using an Economic Deep Learning Model

TL;DR

This paper introduces an economic deep learning model for detecting IoT botnet attacks, achieving higher accuracy with less resource usage and faster training compared to existing methods.

Contribution

It presents a novel deep learning-based approach optimized for IoT security that balances detection performance and implementation cost.

Findings

Higher detection accuracy than state-of-the-art models

Reduced implementation budget for deployment

Faster training and detection processes

Abstract

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.