mlsanityData to 7 Oct 2026

SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection

arXiv 2404.09481 ↗First posted 15 Apr 2024cs.CR cs.LGcited by 3

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Abstract (arXiv)

In this study, we introduce SpamDam, a SMS spam detection framework designed to overcome key challenges in detecting and understanding SMS spam, such as the lack of public SMS spam datasets, increasing privacy concerns of collecting SMS data, and the need for adversary-resistant detection models. SpamDam comprises four innovative modules: an SMS spam radar that identifies spam messages from online social networks(OSNs); an SMS spam inspector for statistical analysis; SMS spam detectors(SSDs) that enable both central training and federated learning; and an SSD analyzer that evaluates model resistance against adversaries in realistic scenarios. Leveraging SpamDam, we have compiled over 76K SMS spam messages from Twitter and Weibo between 2018 and 2023, forming the largest dataset of its kind. This dataset has enabled new insights into recent spam campaigns and the training of high-performing binary and multi-label classifiers for spam detection. Furthermore, effectiveness of federated learning has been well demonstrated to enable privacy-preserving SMS spam detection. Additionally, we have rigorously tested the adversarial robustness of SMS spam detection models, introducing the novel reverse backdoor attack, which has shown effectiveness and stealthiness in practical tests.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 36 of its 69 references that are in the map. First posted on arXiv 15 Apr 2024.

Builds on

36 of 69 references.

References · 36

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24 more
  1. 2018Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifierscited by 341
  2. 2020Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networkscited by 341
  3. 2020BAE: BERT-based Adversarial Examples for Text Classificationcited by 316
  4. 2017Certified Defenses for Data Poisoning Attackscited by 288
  5. 2018Adversarial Example Generation with Syntactically Controlled Paraphrase Networkscited by 282
  6. 2018Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learningcited by 219
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  12. 2022Truth Serum: Poisoning Machine Learning Models to Reveal Their Secretscited by 94
  13. 2021Bad Characters: Imperceptible NLP Attackscited by 69
  14. 2017Online Human-Bot Interactions: Detection, Estimation, and Characterizationcited by 64
  15. 2020Comparing BERT against traditional machine learning text classificationcited by 45
  16. 2020Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencodercited by 27
  17. 2020Natural Backdoor Attack on Text Datacited by 18
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  20. 2022Prompt-Learning for Short Text Classificationcited by 11
  21. 2023Adversarial Clean Label Backdoor Attacks and Defenses on Text Classification Systemscited by 10
  22. 2022Clues in Tweets: Twitter-Guided Discovery and Analysis of SMS Spamcited by 5
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  24. 2023Concept Drift and Long-Tailed Distribution in Fine-Grained Visual Categorization: Benchmark and Methodcited by 2

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025StructTransform: A Scalable Attack Surface for Safety-Aligned Large Language Modelscited by 2
  2. 2025Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Modelscited by 1
  3. 2025Beyond One-Size-Fits-All: Personalized Harmful Content Detection with In-Context Learningcited by 0