# Towards a Universal Features Set for IoT Botnet Attacks Detection

arXiv: https://arxiv.org/abs/2012.00463 · HTML: https://mlsanity.com/paper/2012.00463/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2020.

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

- [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)
- [Class-Proportional Coreset Selection for Difficulty-Separable Data](https://mlsanity.com/paper/2507.10904/) (2025)
- [Associated Random Neural Networks for Collective Classification of Nodes in Botnet Attacks](https://mlsanity.com/paper/2303.13627/) (2023)

## Abstract (arXiv)

The security pitfalls of IoT devices make it easy for the attackers to exploit the IoT devices and make them a part of a botnet. Once hundreds of thousands of IoT devices are compromised and become the part of a botnet, the attackers use this botnet to launch the large and complex distributed denial of service (DDoS) attacks which take down the target websites or services and make them unable to respond the legitimate users. So far, many botnet detection techniques have been proposed but their performance is limited to a specific dataset on which they are trained. This is because the features used to train a machine learning model on one botnet dataset, do not perform well on other datasets due to the diversity of attack patterns. Therefore, in this paper, we propose a universal features set to better detect the botnet attacks regardless of the underlying dataset. The proposed features set manifest preeminent results for detecting the botnet attacks when tested the trained machine learning models over three different botnet attack datasets.