# Collaborative Domain Blocking: Using federated NLP To Detect Malicious Domains

arXiv: https://arxiv.org/abs/2210.04088 · HTML: https://mlsanity.com/paper/2210.04088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 28 references that are in the map.
- First posted on arXiv 8 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [Salvaging Federated Learning by Local Adaptation](https://mlsanity.com/paper/2002.04758/) (2020)
- [BERT: A Review of Applications in Natural Language Processing and Understanding](https://mlsanity.com/paper/2103.11943/) (2021)
- [Ad-Blocking and Counter Blocking: A Slice of the Arms Race](https://mlsanity.com/paper/1605.05077/) (2016)
- [Filter List Generation for Underserved Regions](https://mlsanity.com/paper/1910.07303/) (2019)
- [Percival: Making In-Browser Perceptual Ad Blocking Practical With Deep Learning](https://mlsanity.com/paper/1905.07444/) (2019)
- [Characterizing Smart Home IoT Traffic in the Wild](https://mlsanity.com/paper/2001.08288/) (2020)
- [The CNAME of the Game: Large-scale Analysis of DNS-based Tracking Evasion](https://mlsanity.com/paper/2102.09301/) (2021)
- [AutoFR: Automated Filter Rule Generation for Adblocking](https://mlsanity.com/paper/2202.12872/) (2022)

## Abstract (arXiv)

Current content filtering and blocking methods are susceptible to various circumvention techniques and are relatively slow in dealing with new threats. This is due to these methods using shallow pattern recognition that is based on regular expression rules found in crowdsourced block lists. We propose a novel system that aims to remedy the aforementioned issues by examining deep textual patterns of network-oriented content relating to the domain being interacted with. Moreover, we propose to use federated learning that allows users to take advantage of each other's localized knowledge/experience regarding what should or should not be blocked on a network without compromising privacy. Our experiments show the promise of our proposed approach in real world settings. We also provide data-driven recommendations on how to best implement the proposed system.