# An Adversarial Attack Analysis on Malicious Advertisement URL Detection Framework

arXiv: https://arxiv.org/abs/2204.13172 · HTML: https://mlsanity.com/paper/2204.13172/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 27 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [Adversarial Attack on Community Detection by Hiding Individuals](https://mlsanity.com/paper/2001.07933/) (2020)
- [Malicious URL Detection using Machine Learning: A Survey](https://mlsanity.com/paper/1701.07179/) (2017)
- [A Survey of Machine Learning Techniques in Adversarial Image Forensics](https://mlsanity.com/paper/2010.09680/) (2020)
- [Feature Importance Guided Attack: A Model Agnostic Adversarial Attack](https://mlsanity.com/paper/2106.14815/) (2021)
- [Demystifying the Transferability of Adversarial Attacks in Computer Networks](https://mlsanity.com/paper/2110.04488/) (2021)
- [PhishAri: Automatic Realtime Phishing Detection on Twitter](https://mlsanity.com/paper/1301.6899/) (2013)
- [Reliability and Robustness analysis of Machine Learning based Phishing URL Detectors](https://mlsanity.com/paper/2005.08454/) (2020)

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

- [LLMs are One-Shot URL Classifiers and Explainers](https://mlsanity.com/paper/2409.14306/) (2024)
- [Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization](https://mlsanity.com/paper/2209.04930/) (2022)
- [Employing Deep Ensemble Learning for Improving the Security of Computer Networks against Adversarial Attacks](https://mlsanity.com/paper/2209.12195/) (2022)
- [Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees](https://mlsanity.com/paper/2403.02995/) (2024)
- [From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories](https://mlsanity.com/paper/2504.16449/) (2025)

## Abstract (arXiv)

Malicious advertisement URLs pose a security risk since they are the source of cyber-attacks, and the need to address this issue is growing in both industry and academia. Generally, the attacker delivers an attack vector to the user by means of an email, an advertisement link or any other means of communication and directs them to a malicious website to steal sensitive information and to defraud them. Existing malicious URL detection techniques are limited and to handle unseen features as well as generalize to test data. In this study, we extract a novel set of lexical and web-scrapped features and employ machine learning technique to set up system for fraudulent advertisement URLs detection. The combination set of six different kinds of features precisely overcome the obfuscation in fraudulent URL classification. Based on different statistical properties, we use twelve different formatted datasets for detection, prediction and classification task. We extend our prediction analysis for mismatched and unlabelled datasets. For this framework, we analyze the performance of four machine learning techniques: Random Forest, Gradient Boost, XGBoost and AdaBoost in the detection part. With our proposed method, we can achieve a false negative rate as low as 0.0037 while maintaining high accuracy of 99.63%. Moreover, we devise a novel unsupervised technique for data clustering using K- Means algorithm for the visual analysis. This paper analyses the vulnerability of decision tree-based models using the limited knowledge attack scenario. We considered the exploratory attack and implemented Zeroth Order Optimization adversarial attack on the detection models.