推文中的线索：基于Twitter引导的短信垃圾邮件发现与分析
Clues in Tweets: Twitter-Guided Discovery and Analysis of SMS Spam
AI总结：
本研究提出SpamHunter系统，从Twitter大规模收集短信垃圾邮件数据，构建了含21,918条消息、75种语言的最大公开数据集，并评估现有对抗措施的不足。
摘要：
短信（SMS，短消息服务）在通过移动设备进行业务和服务交付中扮演关键角色，但长期以来一直被滥用于发送垃圾邮件，如今这一问题可能因A2P（应用到个人）批量消息的出现而仍在加剧。由于缺乏关于非法活动的最新信息，控制短信垃圾邮件的努力受到阻碍。在我们的研究中，我们提出了一种新颖的解决方案，从Twitter上大规模收集最新的短信垃圾邮件数据，用户会在Twitter上自愿报告他们收到的垃圾邮件。为此，我们设计并实现了SpamHunter，一个自动化流水线，用于发现报告短信垃圾邮件的推文并从附带的截图中提取消息内容。利用SpamHunter，我们从Twitter收集了一个包含21,918条短信垃圾邮件消息的数据集，涵盖75种语言，时间跨度超过四年。据我们所知，这是迄今公开的最大的短信垃圾邮件数据集。更重要的是，SpamHunter使我们能够持续监控新出现的短信垃圾邮件消息，这有助于持续缓解短信垃圾邮件问题的努力。我们还进行了一项深入的测量研究，揭示了垃圾邮件发送者策略、基础设施和垃圾邮件活动的新趋势。我们还利用我们的垃圾短信数据来评估短信生态系统（包括反垃圾邮件服务、批量短信服务和文本消息应用）所采取的垃圾邮件对抗措施的稳健性。我们的评估表明，这些保护措施无法有效处理这些垃圾邮件样本：要么引入显著的误报，要么漏掉大量新报告的垃圾邮件消息。
英文摘要：
With its critical role in business and service delivery through mobile devices, SMS (Short Message Service) has long been abused for spamming, which is still on the rise today possibly due to the emergence of A2P bulk messaging. The effort to control SMS spam has been hampered by the lack of up-to-date information about illicit activities. In our research, we proposed a novel solution to collect recent SMS spam data, at a large scale, from Twitter, where users voluntarily report the spam messages they receive. For this purpose, we designed and implemented SpamHunter, an automated pipeline to discover SMS spam reporting tweets and extract message content from the attached screenshots. Leveraging SpamHunter, we collected from Twitter a dataset of 21,918 SMS spam messages in 75 languages, spanning over four years. To our best knowledge, this is the largest SMS spam dataset ever made public. More importantly, SpamHunter enables us to continuously monitor emerging SMS spam messages, which facilitates the ongoing effort to mitigate SMS spamming. We also performed an in-depth measurement study that sheds light on the new trends in the spammer's strategies, infrastructure and spam campaigns. We also utilized our spam SMS data to evaluate the robustness of the spam countermeasures put in place by the SMS ecosystem, including anti-spam services, bulk SMS services, and text messaging apps. Our evaluation shows that such protection cannot effectively handle those spam samples: either introducing significant false positives or missing a large number of newly reported spam messages.
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。
