# NFDLM: A Lightweight Network Flow based Deep Learning Model for DDoS Attack Detection in IoT Domains

arXiv: https://arxiv.org/abs/2207.10803 · HTML: https://mlsanity.com/paper/2207.10803/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 40 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [LSTM: A Search Space Odyssey](https://mlsanity.com/paper/1503.04069/) (2015)
- [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)
- [Mutual Information, Neural Networks and the Renormalization Group](https://mlsanity.com/paper/1704.06279/) (2017)
- [IoT Security Techniques Based on Machine Learning](https://mlsanity.com/paper/1801.06275/) (2018)
- [LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks](https://mlsanity.com/paper/2207.00424/) (2022)

## Built on by (most cited first, 3 of 3)

- [Impact of Latent Space Dimension on IoT Botnet Detection Performance: VAE-Encoder Versus ViT-Encoder](https://mlsanity.com/paper/2504.14879/) (2025)
- [Enhancing IoT-Botnet Detection using Variational Auto-encoder and Cost-Sensitive Learning: A Deep Learning Approach for Imbalanced Datasets](https://mlsanity.com/paper/2505.01437/) (2025)
- [On the Impact of Entropy-based Features](https://mlsanity.com/paper/2607.15379/) (2026)

## Abstract (arXiv)

In the recent years, Distributed Denial of Service (DDoS) attacks on Internet of Things (IoT) devices have become one of the prime concerns to Internet users around the world. One of the sources of the attacks on IoT ecosystems are botnets. Intruders force IoT devices to become unavailable for its legitimate users by sending large number of messages within a short interval. This study proposes NFDLM, a lightweight and optimised Artificial Neural Network (ANN) based Distributed Denial of Services (DDoS) attack detection framework with mutual correlation as feature selection method which produces a superior result when compared with Long Short Term Memory (LSTM) and simple ANN. Overall, the detection performance achieves approximately 99% accuracy for the detection of attacks from botnets. In this work, we have designed and compared four different models where two are based on ANN and the other two are based on LSTM to detect the attack types of DDoS.