论文标题
可疑邮件的深质量
DeepQuarantine for Suspicious Mail
论文作者
论文摘要
在本文中，我们引入了DeepQuarantine（DQ），这是一种云技术，可检测和隔离潜在的垃圾邮件消息。垃圾邮件攻击变得越来越多样化，可能对电子邮件用户有害。尽管垃圾邮件过滤系统的质量高质量和性能，但检测垃圾邮件活动可能需要一些时间。不幸的是，在这种情况下，一些不需要的消息将传递给用户。为了解决此问题，我们创建了DQ，该DQ检测到潜在的垃圾邮件并将其保存在特殊的隔离文件夹中一段时间。收获的时间使我们能够仔细检查消息以提高反垃圾邮件解决方案的可靠性。由于技术的高精度，大多数隔离邮件都是垃圾邮件，它允许客户毫不延迟使用电子邮件。我们的解决方案基于在MIME标头上应用卷积神经网络以从大规模历史数据中提取深度特征。我们评估了关于现实世界数据的建议方法，并表明DQ增强了垃圾邮件检测的质量。
In this paper, we introduce DeepQuarantine (DQ), a cloud technology to detect and quarantine potential spam messages. Spam attacks are becoming more diverse and can potentially be harmful to email users. Despite the high quality and performance of spam filtering systems, detection of a spam campaign can take some time. Unfortunately, in this case some unwanted messages get delivered to users. To solve this problem, we created DQ, which detects potential spam and keeps it in a special Quarantine folder for a while. The time gained allows us to double-check the messages to improve the reliability of the anti-spam solution. Due to high precision of the technology, most of the quarantined mail is spam, which allows clients to use email without delay. Our solution is based on applying Convolutional Neural Networks on MIME headers to extract deep features from large-scale historical data. We evaluated the proposed method on real-world data and showed that DQ enhances the quality of spam detection.
