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    Anti-spam transient entity classification
    11.
    发明授权
    Anti-spam transient entity classification 有权
    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。

    DETECTING SPAM FROM A BULK REGISTERED E-MAIL ACCOUNT
    12.
    发明申请
    DETECTING SPAM FROM A BULK REGISTERED E-MAIL ACCOUNT 有权
    从大量注册的电子邮件帐户检测垃圾邮件

    公开(公告)号：US20100077043A1

    公开(公告)日：2010-03-25

    申请号：US12562792

    申请日：2009-09-18

    申请人： Vishwanath Tumkur Ramarao ,  Mark E. Risher ,  Xiaopeng Xi

    发明人： Vishwanath Tumkur Ramarao ,  Mark E. Risher ,  Xiaopeng Xi

    IPC分类号： G06F15/16

    CPC分类号： H04L12/585 ,  H04L51/12 ,  H04L63/1425

    摘要： The invention provides for at least three processes for detecting the probability of abusive use of a message account for sending large amounts of unsolicited messages, such as spam, to other message accounts. For example, information provided at registration for a new message account can be processed to determine the likelihood of abusive use of that message account. Also, inbound messages can be processed to determine if the message account that sent the inbound message is abusing the use of that message account. Additionally, outbound messages can be processed to determine if the message account that is attempting to send an outbound message is abusing the use of that message account. Each of these three processes can operate separately or in any combination with each other to further improve the probability that abusive use of a message account will be detected promptly and accurately.

    摘要翻译： 本发明提供至少三个过程，用于检测滥用于消息帐户以将大量未经请求的消息（例如垃圾邮件）发送到其他消息帐户的可能性的过程。 例如，可以处理在注册新消息帐户时提供的信息，以确定滥用该消息帐户的可能性。 此外，可以处理入站邮件以确定发送入站邮件的邮件帐户是否滥用该邮件帐户的使用。 此外，可以处理出站邮件，以确定尝试发送出站邮件的邮件帐户是否滥用该邮件帐户的使用。 这三个进程中的每一个可以分开地或彼此以任何组合的方式进行操作，以进一步提高迅速且准确地检测到滥用消息账户的可能性。

    User trustworthiness
    13.
    发明授权
    User trustworthiness 有权
    用户可信赖性

    公开(公告)号：US09519682B1

    公开(公告)日：2016-12-13

    申请号：US13117037

    申请日：2011-05-26

    申请人： Jay Pujara ,  Vishwanath Tumkur Ramarao ,  Xiaopeng Xi ,  Martin Zinkevich ,  Anirban Dasgupta ,  Belle Tseng ,  Wei Chu ,  Jyh-Shin Gareth Shue

    发明人： Jay Pujara ,  Vishwanath Tumkur Ramarao ,  Xiaopeng Xi ,  Martin Zinkevich ,  Anirban Dasgupta ,  Belle Tseng ,  Wei Chu ,  Jyh-Shin Gareth Shue

    IPC分类号： G06F11/00 ,  G06F17/30 ,  G06Q10/10 ,  H04H20/28 ,  H04H60/82 ,  G06F12/14

    CPC分类号： G06F17/3053 ,  G06Q10/10 ,  H04H20/28 ,  H04H60/82

    摘要： Embodiments are directed towards generating a unified user account trustworthiness system through user account trustworthiness scores. A trusted group of user accounts may be identified for a given action by grouping a plurality of user accounts into tiers based on a trustworthiness score of each user account for the given action. The tiers and/or trustworthiness scores may be employed to classify an item, such as a message as spam or non-spam, based on input from the user accounts. The trustworthiness scores may also be employed to determine if a user account is a robot account or a human account. The trusted group for a given action may dynamically evolve over time by regrouping the user accounts based on modified trustworthiness scores. A trustworthiness score of an individual user account may be modified based on input received from the individual user account and input from other user accounts.

    摘要翻译： 实施例旨在通过用户帐户可信度得分来生成统一的用户帐户可信赖性系统。 可以基于针对给定动作的每个用户帐户的可信度分数将多个用户帐户分组成层，可以为给定动作识别可信赖的用户帐户组。 层级和/或可信赖性分数可以用于基于来自用户帐户的输入来将项目（诸如作为垃圾邮件或非垃圾邮件）的消息分类。 还可以使用可信度分数来确定用户帐户是机器人帐户还是人类账户。 给定动作的受信任组可以通过基于修改的可信度得分重新分组用户账户而随着时间的推移而动态演变。 可以基于从单个用户帐户接收的输入和来自其他用户帐户的输入来修改个人用户帐户的可信度分数。

    FILTER FOR BLOCKING IMAGE-BASED SPAM
    15.
    发明申请
    FILTER FOR BLOCKING IMAGE-BASED SPAM 有权
    用于阻止基于图像的垃圾邮件的过滤器

    公开(公告)号：US20090220166A1

    公开(公告)日：2009-09-03

    申请号：US12039310

    申请日：2008-02-28

    申请人： Jaesik Choi ,  Ke Wei ,  Vishwanath Tumkur Ramarao

    发明人： Jaesik Choi ,  Ke Wei ,  Vishwanath Tumkur Ramarao

    IPC分类号： G06K9/40

    CPC分类号： G06K9/38 ,  G06K2209/01 ,  H04L51/12

    摘要： A network device and method are directed towards detecting and blocking image spam within a message by employing a weighted min-hash to perform a near duplicate detection (NDD) of determined features within an image as compared to known spam images. The weighting for the min-hash is determined based on employing a machine learning algorithm, such as a perceptron, to identify an importance of each bit in a signature vector of the image. The signature vector is generated by extracting a shape of text in the image using a Discrete Cosine Transform, extracting low-frequency characteristics using a high-pass filter, and then performing various morphological operations to emphasize the shape of the text and reduce noise. Selected feature bits are extracted from the lowest frequency and intensity bits of the resulting signal to generate the signature vector used in the weighted min-hash NDD.

    摘要翻译： 网络设备和方法旨在通过采用加权最小散列来与图像中已知的垃圾邮件图像相比，在图像内执行确定的特征的近似重复检测（NDD）来检测和阻止消息内的图像垃圾邮件。 基于使用机器学习算法（例如感知器）来确定最小散列的加权，以识别图像的签名矢量中每个位的重要性。 通过使用离散余弦变换提取图像中的文本的形状，使用高通滤波器提取低频特性，然后进行各种形态操作以强调文本的形状并降低噪声来生成签名向量。 从所得信号的最低频率和强度比特中提取所选特征位，以产生在加权最小散列NDD中使用的签名向量。