SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam
  Detection
Yekai Li Rufan Zhang Wenxin Rong Xianghang Mi
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.