# Tranco: A Research-Oriented Top Sites Ranking Hardened Against Manipulation

arXiv: https://arxiv.org/abs/1806.01156 · HTML: https://mlsanity.com/paper/1806.01156/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2018.

## Built on by (most cited first, 30 of 63)

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- [A Survey on Malware Detection with Graph Representation Learning](https://mlsanity.com/paper/2303.16004/) (2023)
- [Feature Importance Guided Attack: A Model Agnostic Adversarial Attack](https://mlsanity.com/paper/2106.14815/) (2021)
- [Large Language Models for Security Operations Centers: A Comprehensive Survey](https://mlsanity.com/paper/2509.10858/) (2025)
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- [Multimodal Large Language Models for Phishing Webpage Detection and Identification](https://mlsanity.com/paper/2408.05941/) (2024)
- [Source Coverage and Citation Bias in LLM-based vs. Traditional Search Engines](https://mlsanity.com/paper/2512.09483/) (2025)
- [Indirect Prompt Injection in the Wild: An Empirical Study of Prevalence, Techniques, and Objectives](https://mlsanity.com/paper/2604.27202/) (2026)
- [PhishSim: Aiding Phishing Website Detection with a Feature-Free Tool](https://mlsanity.com/paper/2207.10801/) (2022)
- [DomURLs_BERT: Pre-trained BERT-based Model for Malicious Domains and URLs Detection and Classification](https://mlsanity.com/paper/2409.09143/) (2024)
- [TrapFlow: Controllable Website Fingerprinting Defense via Dynamic Backdoor Learning](https://mlsanity.com/paper/2412.11471/) (2024)
- [CELEST: Federated Learning for Globally Coordinated Threat Detection](https://mlsanity.com/paper/2205.11459/) (2022)
- [PLAtE: A Large-scale Dataset for List Page Web Extraction](https://mlsanity.com/paper/2205.12386/) (2022)
- [False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA Classifiers](https://mlsanity.com/paper/2307.04358/) (2023)
- [Automating Governing Knowledge Commons and Contextual Integrity (GKC-CI) Privacy Policy Annotations with Large Language Models](https://mlsanity.com/paper/2311.02192/) (2023)
- [PhishDebate: An LLM-Based Multi-Agent Framework for Phishing Website Detection](https://mlsanity.com/paper/2506.15656/) (2025)
- [AutoFR: Automated Filter Rule Generation for Adblocking](https://mlsanity.com/paper/2202.12872/) (2022)
- [STAR: Semantic-Traffic Alignment and Retrieval for Zero-Shot HTTPS Website Fingerprinting](https://mlsanity.com/paper/2512.17667/) (2025)
- [How Generative AI Disrupts Search: An Empirical Study of Google Search, Gemini, and AI Overviews](https://mlsanity.com/paper/2604.27790/) (2026)
- [ResAware: Cross-Environment Website Fingerprinting via Resource-Privileged Distillation](https://mlsanity.com/paper/2606.17462/) (2026)
- [UI-Venus-2 Technical Report](https://mlsanity.com/paper/2609.00028/) (2026)
- [The More, the Better? A Study on Collaborative Machine Learning for DGA Detection](https://mlsanity.com/paper/2109.11830/) (2021)
- [PURL: Safe and Effective Sanitization of Link Decoration](https://mlsanity.com/paper/2308.03417/) (2023)
- [Targeted and Troublesome: Tracking and Advertising on Children's Websites](https://mlsanity.com/paper/2308.04887/) (2023)
- [Automatically Detecting Online Deceptive Patterns](https://mlsanity.com/paper/2411.07441/) (2024)

## Abstract (arXiv)

In order to evaluate the prevalence of security and privacy practices on a representative sample of the Web, researchers rely on website popularity rankings such as the Alexa list. While the validity and representativeness of these rankings are rarely questioned, our findings show the contrary: we show for four main rankings how their inherent properties (similarity, stability, representativeness, responsiveness and benignness) affect their composition and therefore potentially skew the conclusions made in studies. Moreover, we find that it is trivial for an adversary to manipulate the composition of these lists. We are the first to empirically validate that the ranks of domains in each of the lists are easily altered, in the case of Alexa through as little as a single HTTP request. This allows adversaries to manipulate rankings on a large scale and insert malicious domains into whitelists or bend the outcome of research studies to their will. To overcome the limitations of such rankings, we propose improvements to reduce the fluctuations in list composition and guarantee better defenses against manipulation. To allow the research community to work with reliable and reproducible rankings, we provide Tranco, an improved ranking that we offer through an online service available at https://tranco-list.eu.