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    • 1. 发明授权
    • De-duplication in billing system
    • 计费系统中的重复数据删除
    • US08380736B2
    • 2013-02-19
    • US12785313
    • 2010-05-21
    • Malcolm Erik PearsonMatthew Charles Setzer
    • Malcolm Erik PearsonMatthew Charles Setzer
    • G06F7/00G06F17/30
    • G06F17/30371G06Q20/14G06Q20/401G06Q20/4016G06Q30/04
    • Scalable handling of billing events that affect one or more accounts. A computing system partitions received events into a number of channels, perhaps by account identifier. The channels receive the events, and perform de-duplication of the events. This de-duplication may be performed using a Bloom filter that is updated to reflect the receipt of any original event. The Bloom filter may be used to either determine that the event is not a duplicate of another, or to determine that the event cannot be ruled out as being a duplicate of another. In the latter case, further processing may be performed to for definitively determine whether the event is truly a duplication, or in the alternative, the event may be immediately treated as a duplicate.
    • 影响一个或多个帐户的结算事件的可扩展处理。 计算系统可以通过帐户标识符将接收到的事件分成多个信道。 频道接收事件,并执行事件的重复数据删除。 可以使用更新以反映任何原始事件的接收的布隆过滤器来执行该重复删除。 Bloom过滤器可以用于确定事件不是与另一个事件重复,或者确定事件不能被排除为与另一个事件重复。 在后一种情况下,可以执行进一步的处理以便确定该事件是否是真正的重复,或者替代地,该事件可以立即被视为重复。
    • 6. 发明申请
    • DE-DUPLICATION IN BILLING SYSTEM
    • 结算系统中的重用
    • US20110289102A1
    • 2011-11-24
    • US12785313
    • 2010-05-21
    • Malcolm Erik PearsonMatthew Charles Setzer
    • Malcolm Erik PearsonMatthew Charles Setzer
    • G06F17/30
    • G06F17/30371G06Q20/14G06Q20/401G06Q20/4016G06Q30/04
    • Scalable handling of billing events that affect one or more accounts. A computing system partitions received events into a number of channels, perhaps by account identifier. The channels receive the events, and perform de-duplication of the events. This de-duplication may be performed using a Bloom filter that is updated to reflect the receipt of any original event. The Bloom filter may be used to either determine that the event is not a duplicate of another, or to determine that the event cannot be ruled out as being a duplicate of another. In the latter case, further processing may be performed to for definitively determine whether the event is truly a duplication, or in the alternative, the event may be immediately treated as a duplicate.
    • 影响一个或多个帐户的结算事件的可扩展处理。 计算系统可以通过帐户标识符将接收到的事件分成多个信道。 频道接收事件,并执行事件的重复数据删除。 可以使用更新以反映任何原始事件的接收的布隆过滤器来执行该重复删除。 Bloom过滤器可以用于确定事件不是与另一个事件重复,或者确定事件不能被排除为与另一个事件重复。 在后一种情况下,可以执行进一步的处理以便确定该事件是否是真正的重复,或者替代地,该事件可以立即被视为重复。
    • 8. 发明授权
    • Determination of participation in a malicious software campaign
    • 决定参与恶意软件运动
    • US07899870B2
    • 2011-03-01
    • US11767860
    • 2007-06-25
    • Malcolm Erik PearsonMihai Costea
    • Malcolm Erik PearsonMihai Costea
    • G06F15/16
    • H04L63/1408H04L51/12H04L2463/144
    • Sources of spam, such as botnets, are detected by analyzing message traffic for behavioral patterns and indications of suspicious content. The content of a known malicious source is analyzed. Message traffic associated with the known malicious source is analyzed. Associated message traffic includes messages sent directly from the known malicious source to recipients, and messages sent from the recipients to subsequent direct and indirect recipients. Portions of the content of the known malicious source are selected and content of associated message traffic is analyzed for an indication of the selected content. If the selected content is found in the content of a message, the source of the message is determined to be a source of spam. Associated message traffic is additionally analyzed for behavioral patterns, such as anomalies and/or flurries of activity, to determine a potential malicious source.
    • 通过分析用于行为模式的消息流量和可疑内容的指示来检测诸如僵尸网络之类的垃圾邮件来源。 分析已知恶意源的内容。 分析与已知恶意源相关联的消息流量。 相关消息流量包括从已知恶意源直接发送给收件人的消息,以及从收件人发送到后续直接和间接收件人的消息。 选择已知恶意源的内容的部分,并且分析相关联的消息业务的内容以选择内容的指示。 如果在消息的内容中找到所选择的内容,则将消息的来源确定为垃圾邮件的来源。 另外分析相关的消息流量以用于行为模式,例如异常和/或活动的波动,以确定潜在的恶意源。
    • 9. 发明申请
    • Determination Of Participation In A Malicious Software Campaign
    • 确定参与恶意软件运动
    • US20080320095A1
    • 2008-12-25
    • US11767860
    • 2007-06-25
    • Malcolm Erik PearsonMihai Costea
    • Malcolm Erik PearsonMihai Costea
    • G06F15/16
    • H04L63/1408H04L51/12H04L2463/144
    • Sources of spam, such as botnets, are detected by analyzing message traffic for behavioral patterns and indications of suspicious content. The content of a known malicious source is analyzed. Message traffic associated with the known malicious source is analyzed. Associated message traffic includes messages sent directly from the known malicious source to recipients, and messages sent from the recipients to subsequent direct and indirect recipients. Portions of the content of the known malicious source are selected and content of associated message traffic is analyzed for an indication of the selected content. If the selected content is found in the content of a message, the source of the message is determined to be a source of spam. Associated message traffic is additionally analyzed for behavioral patterns, such as anomalies and/or flurries of activity, to determine a potential malicious source.
    • 通过分析用于行为模式的消息流量和可疑内容的指示来检测诸如僵尸网络之类的垃圾邮件来源。 分析已知恶意源的内容。 分析与已知恶意源相关联的消息流量。 相关消息流量包括从已知恶意源直接发送给收件人的消息,以及从收件人发送到后续直接和间接收件人的消息。 选择已知恶意源的内容的部分,并且分析相关联的消息业务的内容以选择内容的指示。 如果在消息的内容中找到所选择的内容,则将消息的来源确定为垃圾邮件的来源。 另外分析相关的消息流量以用于行为模式,例如异常和/或活动的波动,以确定潜在的恶意源。