# IoT Botnet Detection Using an Economic Deep Learning Model

arXiv: https://arxiv.org/abs/2302.02013 · HTML: https://mlsanity.com/paper/2302.02013/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 87 references that are in the map.
- First posted on arXiv 3 Feb 2023.

## Builds on (most cited first, 11 of 11)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [Deep Gated Recurrent and Convolutional Network Hybrid Model for Univariate Time Series Classification](https://mlsanity.com/paper/1812.07683/) (2018)
- [Machine Learning in IoT Security: Current Solutions and Future Challenges](https://mlsanity.com/paper/1904.05735/) (2019)
- [Turning Internet of Things(IoT) into Internet of Vulnerabilities (IoV) : IoT Botnets](https://mlsanity.com/paper/1702.03681/) (2017)
- [Towards Developing Network forensic mechanism for Botnet Activities in the IoT based on Machine Learning Techniques](https://mlsanity.com/paper/1711.02825/) (2017)
- [Modeling, Analysis, and Mitigation of Dynamic Botnet Formation in Wireless IoT Networks](https://mlsanity.com/paper/1901.00741/) (2018)
- [Autonomous Low Power IoT System Architecture for Cybersecurity Monitoring](https://mlsanity.com/paper/2106.00834/) (2021)
- [Application Layer Coding for IoT: Benefits, Limitations, and Implementation Aspects](https://mlsanity.com/paper/1706.01271/) (2017)

## Built on by (most cited first, 1 of 1)

- [Dimensionality Reduction for Cyberattack Classification: A Comparative Evaluation of PCA and Linear Predictive Coding](https://mlsanity.com/paper/2606.05584/) (2026)

## Abstract (arXiv)

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.