# DNS Typo-squatting Domain Detection: A Data Analytics & Machine Learning Based Approach

arXiv: https://arxiv.org/abs/2012.13604 · HTML: https://mlsanity.com/paper/2012.13604/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2020.

## Built on by (most cited first, 8 of 8)

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- [An Attention-based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2201.09172/) (2022)
- [On TinyML and Cybersecurity: Electric Vehicle Charging Infrastructure Use Case](https://mlsanity.com/paper/2404.16894/) (2024)
- [An Automated Data Engineering Pipeline for Anomaly Detection of IoT Sensor Data](https://mlsanity.com/paper/2109.13828/) (2021)
- [A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service Inference](https://mlsanity.com/paper/2304.00083/) (2023)

## Abstract (arXiv)

Domain Name System (DNS) is a crucial component of current IP-based networks as it is the standard mechanism for name to IP resolution. However, due to its lack of data integrity and origin authentication processes, it is vulnerable to a variety of attacks. One such attack is Typosquatting. Detecting this attack is particularly important as it can be a threat to corporate secrets and can be used to steal information or commit fraud. In this paper, a machine learning-based approach is proposed to tackle the typosquatting vulnerability. To that end, exploratory data analytics is first used to better understand the trends observed in eight domain name-based extracted features. Furthermore, a majority voting-based ensemble learning classifier built using five classification algorithms is proposed that can detect suspicious domains with high accuracy. Moreover, the observed trends are validated by studying the same features in an unlabeled dataset using K-means clustering algorithm and through applying the developed ensemble learning classifier. Results show that legitimate domains have a smaller domain name length and fewer unique characters. Moreover, the developed ensemble learning classifier performs better in terms of accuracy, precision, and F-score. Furthermore, it is shown that similar trends are observed when clustering is used. However, the number of domains identified as potentially suspicious is high. Hence, the ensemble learning classifier is applied with results showing that the number of domains identified as potentially suspicious is reduced by almost a factor of five while still maintaining the same trends in terms of features' statistics.