用合成音节袋算法实时检测邮件垃圾信息，无需存储和额外资源。
Real-time and Zero-footprint Bag of Synthetic Syllables Algorithm for E-mail Spam Detection Using Subject Line and Short Text Fields
- 将邮件主题转为200维稀疏向量，通过相似度匹配识别垃圾邮件。
- 在真实一天的邮件流量中实现近实时检测，准确率高且资源消耗极低。
- 适合部署于资源受限的邮件网关，尤其适合轻量级实时过滤场景。
当前电子邮件服务面临高可用性要求与高吞吐量、垃圾邮件攻击带来的资源压力。深度学习模型因资源消耗大、处理时间长，难以用于前端实时过滤。而大多数垃圾邮件并不复杂，可被简单算法识别。本文提出一种无持久存储、无需字典或额外硬件的「合成音节袋」算法，对邮件主题等短文本生成约200维稀疏哈希向量，通过余弦或欧氏距离比较相似性，快速发现已知垃圾邮件模式。该算法在真实一天的SMTP流量上验证，具备近实时、近零资源占用特性，可有效减轻深度模型负担。
原文摘要 · Abstract (English)
Contemporary e-mail services have high availability expectations from the customers and are resource-strained because of the high-volume throughput and spam attacks. Deep Machine Learning architectures, which are resource hungry and require off-line processing due to the long processing times, are not acceptable at the front line filters. On the other hand, the bulk of the incoming spam is not sophisticated enough to bypass even the simplest algorithms. While the small fraction of the intelligent, highly mutable spam can be detected only by the deep architectures, the stress on them can be unloaded by the simple near real-time and near zero-footprint algorithms such as the Bag of Synthetic Syllables algorithm applied to the short texts of the e-mail subject lines and other short text fields. The proposed algorithm creates a circa 200 sparse dimensional hash or vector for each e-mail subject line that can be compared for the cosine or euclidean proximity distance to find similarities to the known spammy subjects. The algorithm does not require any persistent storage, dictionaries, additional hardware upgrades or software packages. The performance of the algorithm is presented on the one day of the real SMTP traffic.
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