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SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection
April 16, 2024, 4:44 a.m. | Yekai Li, Rufan Zhang, Wenxin Rong, Xianghang Mi
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 …
abstract arxiv challenges concerns cs.cr cs.lg data datasets detection framework key privacy public sms spam study type understanding
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