# Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets

arXiv: https://arxiv.org/abs/2505.01437 · HTML: https://mlsanity.com/paper/2505.01437/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 29 references that are in the map.
- First posted on arXiv 26 Apr 2025.

## Builds on (most cited first, 7 of 7)

- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [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)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [IoT Security: Botnet detection in IoT using Machine learning](https://mlsanity.com/paper/2104.02231/) (2021)
- [Machine Learning-Based Early Detection of IoT Botnets Using Network-Edge Traffic](https://mlsanity.com/paper/2010.11453/) (2020)
- [Towards a Universal Features Set for IoT Botnet Attacks Detection](https://mlsanity.com/paper/2012.00463/) (2020)
- [NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains](https://mlsanity.com/paper/2207.10803/) (2022)

## Built on by (most cited first, 5 of 5)

- [Graph Attention Neural Network for Botnet Detection: Evaluating Autoencoder, VAE and PCA-Based Dimension Reduction](https://mlsanity.com/paper/2505.17357/) (2025)
- [Are GNNs Worth the Effort for IoT Botnet Detection? A Comparative Study of VAE-GNN vs. ViT-MLP and VAE-MLP Approaches](https://mlsanity.com/paper/2505.17363/) (2025)
- [A Quantized VAE-MLP Botnet Detection Model: A Systematic Evaluation of Quantization-Aware Training and Post-Training Quantization Strategies](https://mlsanity.com/paper/2511.03201/) (2025)
- [Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection via Latent Space Representation Learning and Alignment](https://mlsanity.com/paper/2512.22488/) (2025)
- [How the Graph Construction Technique Shapes Performance in IoT Botnet Detection](https://mlsanity.com/paper/2603.06654/) (2026)

## Abstract (arXiv)

The Internet of Things (IoT) technology has rapidly gained popularity with applications widespread across a variety of industries. However, IoT devices have been recently serving as a porous layer for many malicious attacks to both personal and enterprise information systems with the most famous attacks being botnet-related attacks. The work in this study leveraged Variational Auto-encoder (VAE) and cost-sensitive learning to develop lightweight, yet effective, models for IoT-botnet detection. The aim is to enhance the detection of minority class attack traffic instances which are often missed by machine learning models. The proposed approach is evaluated on a multi-class problem setting for the detection of traffic categories on highly imbalanced datasets. The performance of two deep learning models including the standard feed forward deep neural network (DNN), and Bidirectional-LSTM (BLSTM) was evaluated and both recorded commendable results in terms of accuracy, precision, recall and F1-score for all traffic classes.