# Unsupervised Ensemble Based Deep Learning Approach for Attack Detection in IoT Network

arXiv: https://arxiv.org/abs/2207.07903 · HTML: https://mlsanity.com/paper/2207.07903/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 16 Jul 2022.

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

- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [N-BaIoT: Network-based Detection of IoT Botnet Attacks Using Deep Autoencoders](https://mlsanity.com/paper/1805.03409/) (2018)
- [IoT Security Techniques Based on Machine Learning](https://mlsanity.com/paper/1801.06275/) (2018)
- [Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier](https://mlsanity.com/paper/1904.01352/) (2019)
- [Insight into Insiders and IT: A Survey of Insider Threat Taxonomies, Analysis, Modeling, and Countermeasures](https://mlsanity.com/paper/1805.01612/) (2018)
- [A Lightweight and Scalable Physical Layer Attack Detection Mechanism for the Internet of Things (IoT) Using Hybrid Security Schema](https://mlsanity.com/paper/2103.16920/) (2021)

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

- [Parametric entropy based Cluster Centriod Initialization for k-means clustering of various Image datasets](https://mlsanity.com/paper/2308.07705/) (2023)

## Abstract (arXiv)

The Internet of Things (IoT) has altered living by controlling devices/things over the Internet. IoT has specified many smart solutions for daily problems, transforming cyber-physical systems (CPS) and other classical fields into smart regions. Most of the edge devices that make up the Internet of Things have very minimal processing power. To bring down the IoT network, attackers can utilise these devices to conduct a variety of network attacks. In addition, as more and more IoT devices are added, the potential for new and unknown threats grows exponentially. For this reason, an intelligent security framework for IoT networks must be developed that can identify such threats. In this paper, we have developed an unsupervised ensemble learning model that is able to detect new or unknown attacks in an IoT network from an unlabelled dataset. The system-generated labelled dataset is used to train a deep learning model to detect IoT network attacks. Additionally, the research presents a feature selection mechanism for identifying the most relevant aspects in the dataset for detecting attacks. The study shows that the suggested model is able to identify the unlabelled IoT network datasets and DBN (Deep Belief Network) outperform the other models with a detection accuracy of 97.5% and a false alarm rate of 2.3% when trained using labelled dataset supplied by the proposed approach.