# A Comprehensive Analysis of Adversarial Attacks against Spam Filters

arXiv: https://arxiv.org/abs/2505.03831 · HTML: https://mlsanity.com/paper/2505.03831/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 63 references that are in the map.
- First posted on arXiv 4 May 2025.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [Synthetic and Natural Noise Both Break Neural Machine Translation](https://mlsanity.com/paper/1711.02173/) (2017)
- [Understanding Neural Networks through Representation Erasure](https://mlsanity.com/paper/1612.08220/) (2016)
- [Bad Characters: Imperceptible NLP Attacks](https://mlsanity.com/paper/2106.09898/) (2021)
- [Towards Crafting Text Adversarial Samples](https://mlsanity.com/paper/1707.02812/) (2017)
- [Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection](https://mlsanity.com/paper/1802.03041/) (2018)
- [Greedy Attack and Gumbel Attack: Generating Adversarial Examples for Discrete Data](https://mlsanity.com/paper/1805.12316/) (2018)
- [Adversarial Attacks and Defense on Texts: A Survey](https://mlsanity.com/paper/2005.14108/) (2020)
- [An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach](https://mlsanity.com/paper/2311.04913/) (2023)
- [Spam Detection Using BERT](https://mlsanity.com/paper/2206.02443/) (2022)
- [Analysis and prevention of AI-based phishing email attacks](https://mlsanity.com/paper/2405.05435/) (2024)
- [Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method](https://mlsanity.com/paper/2204.07390/) (2022)

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

- [GCC-Spam: Spam Detection via GAN, Contrastive Learning, and Character Similarity Networks](https://mlsanity.com/paper/2507.14679/) (2025)

## Abstract (arXiv)

Deep learning has revolutionized email filtering, which is critical to protect users from cyber threats such as spam, malware, and phishing. However, the increasing sophistication of adversarial attacks poses a significant challenge to the effectiveness of these filters. This study investigates the impact of adversarial attacks on deep learning-based spam detection systems using real-world datasets. Six prominent deep learning models are evaluated on these datasets, analyzing attacks at the word, character sentence, and AI-generated paragraph-levels. Novel scoring functions, including spam weights and attention weights, are introduced to improve attack effectiveness. This comprehensive analysis sheds light on the vulnerabilities of spam filters and contributes to efforts to improve their security against evolving adversarial threats.