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1. 发明授权

US09442881B1 Anti-spam transient entity classification 有权
标题翻译：反垃圾邮件瞬态实体分类
公开(公告)号：US09442881B1
公开(公告)日：2016-09-13
申请号：US13222720
申请日：2011-08-31
申请人： Sharat Narayan , Vishwanath Tumkur Ramarao , Belle Tseng , Markus Weimer , Young Maeng , Jyh-Shin Shue
发明人： Sharat Narayan , Vishwanath Tumkur Ramarao , Belle Tseng , Markus Weimer , Young Maeng , Jyh-Shin Shue
IPC分类号： G06F15/16
CPC分类号： H04L51/12 , G06F15/16 , G06Q10/107 , H04L51/046 , H04L61/2007 , H04L67/2866
摘要： Embodiments are directed towards multi-level entity classification. An object associated with an entity is received. In one embodiment the object comprises and email and the entity comprises the IP address of a sending email server. If the entity has already been classified, as indicated by an entity classification cache, then a corresponding action is taken on the object. However, if the entity has not been classified, the entity is submitted to a fast classifier for classification. A feature collector concurrently fetches available features, including fast features and full features. The fast classifier classifies the entity based on the fast features, storing the result in the entity classification cache. Subsequent objects associated with the entity are processed based on the cached result of the fast classifier. Then, a full classifier classifies the entity based on at least the full features, storing the result in the entity classification cache.
摘要翻译：实施例针对多级实体分类。接收与实体相关联的对象。在一个实施例中，对象包括和电子邮件，并且实体包括发送电子邮件服务器的IP地址。如果实体已经被分类，如实体分类缓存所示，则对对象采取相应的动作。但是，如果实体尚未分类，则将实体提交给快速分类器进行分类。功能收集器同时提取可用功能，包括快速功能和完整功能。快速分类器基于快速特征对实体进行分类，将结果存储在实体分类缓存中。基于快速分类器的缓存结果处理与实体相关联的后续对象。然后，完整分类器至少基于全部特征对实体进行分类，将结果存储在实体分类缓存中。

2. 发明授权

US08364766B2 Spam filtering based on statistics and token frequency modeling 有权
标题翻译：基于统计和令牌频率建模的垃圾邮件过滤
公开(公告)号：US08364766B2
公开(公告)日：2013-01-29
申请号：US12328723
申请日：2008-12-04
申请人： Lei Zheng , Sharat Narayan , Mark E. Risher , Stanley Ke Wei , Vishwanath Tumkur Ramarao , Anirban Kundu
发明人： Lei Zheng , Sharat Narayan , Mark E. Risher , Stanley Ke Wei , Vishwanath Tumkur Ramarao , Anirban Kundu
IPC分类号： G06F15/16
CPC分类号： H04L51/12 , G06N7/005
摘要： Embodiments are directed towards classifying messages as spam using a two phased approach. The first phase employs a statistical classifier to classify messages based on message content. The second phase targets specific message types to capture dynamic characteristics of the messages and identify spam messages using a token frequency based approach. A client component receives messages and sends them to the statistical classifier, which determines a probability that a message belongs to a particular type of class. The statistical classifier further provides other information about a message, including, a token list, and token thresholds. The message class, token list, and thresholds are provided to the second phase where a number of spam tokens in a given message for a given message class are determined. Based on the threshold, the client component then determines whether the message is spam or non-spam.
摘要翻译：实施例针对使用两阶段方法将消息分类为垃圾邮件。第一阶段采用统计分类器根据消息内容分类消息。第二阶段针对特定的消息类型来捕获消息的动态特征，并使用基于令牌频率的方法识别垃圾邮件。客户端组件接收消息并将其发送到统计分类器，该分类器确定消息属于特定类型的类的概率。统计分类器还提供关于消息的其他信息，包括令牌列表和令牌阈值。消息类别，令牌列表和阈值被提供给第二阶段，其中给定消息类别的给定消息中的多个垃圾邮件令牌被确定。基于阈值，客户端组件然后确定消息是垃圾邮件还是非垃圾邮件。

3. 发明申请

US20100145900A1 SPAM FILTERING BASED ON STATISTICS AND TOKEN FREQUENCY MODELING 有权
标题翻译：基于统计和TOKEN频率建模的垃圾邮件过滤
公开(公告)号：US20100145900A1
公开(公告)日：2010-06-10
申请号：US12328723
申请日：2008-12-04
申请人： Lei Zheng , Sharat Narayan , Mark E. Risher , Stanley Ke Wei , Vishwanath Tumkur Ramarao , Anirban Kundu
发明人： Lei Zheng , Sharat Narayan , Mark E. Risher , Stanley Ke Wei , Vishwanath Tumkur Ramarao , Anirban Kundu
IPC分类号： G06N5/02 , G06F15/16
CPC分类号： H04L51/12 , G06N7/005
摘要： Embodiments are directed towards classifying messages as spam using a two phased approach. The first phase employs a statistical classifier to classify messages based on message content. The second phase targets specific message types to capture dynamic characteristics of the messages and identify spam messages using a token frequency based approach. A client component receives messages and sends them to the statistical classifier, which determines a probability that a message belongs to a particular type of class. The statistical classifier further provides other information about a message, including, a token list, and token thresholds. The message class, token list, and thresholds are provided to the second phase where a number of spam tokens in a given message for a given message class are determined. Based on the threshold, the client component then determines whether the message is spam or non-spam.
摘要翻译：实施例针对使用两阶段方法将消息分类为垃圾邮件。第一阶段采用统计分类器根据消息内容分类消息。第二阶段针对特定的消息类型来捕获消息的动态特征，并使用基于令牌频率的方法识别垃圾邮件。客户端组件接收消息并将其发送到统计分类器，该分类器确定消息属于特定类型的类的概率。统计分类器还提供关于消息的其他信息，包括令牌列表和令牌阈值。消息类别，令牌列表和阈值被提供给第二阶段，其中给定消息类别的给定消息中的多个垃圾邮件令牌被确定。基于阈值，客户端组件然后确定消息是垃圾邮件还是非垃圾邮件。

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