SpamDam：面向隐私保护与抗对抗攻击的SMS垃圾短信检测
SpamDam: Towards Privacy-Preserving and Adversary-Resistant SMS Spam Detection
AI总结：
本研究提出兼具隐私保护与抗对抗性的SMS垃圾短信检测框架SpamDam，构建了超7.6万条的大规模垃圾短信数据集，验证了联邦学习的隐私保护效果并提出新型反向后门攻击以测试模型鲁棒性。
摘要：
本研究提出SpamDam——一款SMS垃圾短信检测框架，旨在解决SMS垃圾短信检测与认知领域的核心挑战：包括公开SMS垃圾短信数据集匮乏、SMS数据收集引发的隐私担忧日益加剧，以及检测模型需具备对抗攻击抗性的需求。SpamDam包含四大创新模块：用于从在线社交网络（OSNs）中识别垃圾短信的SMS垃圾短信雷达、用于统计分析的SMS垃圾短信检验器、支持集中式训练与联邦学习的SMS垃圾短信检测器（SSDs），以及用于评估模型在真实场景下抗对抗攻击能力的SSD分析器。借助SpamDam，我们从2018年至2023年的Twitter与微博中收集了超过7.6万条SMS垃圾短信，构建了目前规模最大的同类数据集。该数据集为研究近期垃圾短信活动提供了新洞见，也支撑了高性能的二分类与多标签垃圾短信检测分类器的训练。此外，研究充分验证了联邦学习在实现隐私保护型SMS垃圾短信检测方面的有效性。我们还严格测试了SMS垃圾短信检测模型的对抗鲁棒性，提出了新型反向后门攻击，该攻击在实际测试中展现出有效性与隐蔽性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
