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    • 4. 发明申请
    • SYSTEM AND METHOD TO PRECISELY LEARN AND ABSTRACT THE POSITIVE FLOW BEHAVIOR OF A UNIFIED COMMUNICATION (UC) APPLICATION AND ENDPOINTS
    • 系统和方法精确学习和抽象统一通信(UC)应用和终点的积极流动行为
    • US20090103524A1
    • 2009-04-23
    • US12254735
    • 2008-10-20
    • Srinivas MantripragadaAmitava Mukherjee
    • Srinivas MantripragadaAmitava Mukherjee
    • H04L12/56
    • H04L65/1079
    • A system and method to precisely learn and enforce security rules for Unified Communication (UC) applications and endpoints is disclosed. According to one embodiment, a behavioral learning system learns and abstracts positive flow behaviors of UC applications and endpoints. The properties of previously received messages from the endpoints and learned behaviors of the plurality of endpoints are stored in a database. A message from a endpoint is received by a message scanner and correlated with the AOR records in the database. The message is classified into one of a whitelist, a blacklist, and a graylist based on the results of analysis by the analysis engine. The whitelist contains the AOR records that are legitimate, the blacklist contains the AOR records that are a potential attack, and the graylist contains the AOR records that belong to neither the whitelist nor the blacklist. Based on the analysis and inspection of the message in light of the learned behaviors, a decision is made to allow, deny, quarantine or redirect the message.
    • 公开了一种用于精确学习和实施统一通信(UC)应用和端点的安全规则的系统和方法。 根据一个实施例,行为学习系统学习和抽象UC应用程序和端点的正流行为。 来自端点的先前接收的消息和多个端点的学习行为的属性被存储在数据库中。 来自端点的消息由消息扫描器接收并与数据库中的AOR记录相关联。 基于分析引擎的分析结果,该消息被分类为白名单,黑名单和灰名单之一。 白名单包含合法的AOR记录,黑名单包含作为潜在攻击的AOR记录,并且灰名单中包含属于白名单和黑名单的AOR记录。 根据学习行为对消息的分析和检查,作出决定允许,拒绝,隔离或重定向消息。
    • 7. 发明授权
    • System and method to precisely learn and abstract the positive flow behavior of a unified communication (UC) application and endpoints
    • 系统和方法来准确地学习和抽象统一通信(UC)应用程序和端点的正向流动行为
    • US08730946B2
    • 2014-05-20
    • US12254735
    • 2008-10-20
    • Srinivas MantripragadaAmitava Mukherjee
    • Srinivas MantripragadaAmitava Mukherjee
    • H04L12/66G06F15/16
    • H04L65/1079
    • A system and method to precisely learn and enforce security rules for Unified Communication (UC) applications and endpoints is disclosed. According to one embodiment, a behavioral learning system learns and abstracts positive flow behaviors of UC applications and endpoints. The properties of previously received messages from the endpoints and learned behaviors of the plurality of endpoints are stored in a database. A message from a endpoint is received by a message scanner and correlated with the AOR records in the database. The message is classified into one of a whitelist, a blacklist, and a graylist based on the results of analysis by the analysis engine. The whitelist contains the AOR records that are legitimate, the blacklist contains the AOR records that are a potential attack, and the graylist contains the AOR records that belong to neither the whitelist nor the blacklist. Based on the analysis and inspection of the message in light of the learned behaviors, a decision is made to allow, deny, quarantine or redirect the message.
    • 公开了一种用于精确学习和实施统一通信(UC)应用和端点的安全规则的系统和方法。 根据一个实施例,行为学习系统学习和抽象UC应用程序和端点的正流行为。 来自端点的先前接收的消息和多个端点的学习行为的属性被存储在数据库中。 来自端点的消息由消息扫描器接收并与数据库中的AOR记录相关联。 基于分析引擎的分析结果,该消息被分类为白名单,黑名单和灰名单之一。 白名单包含合法的AOR记录,黑名单包含作为潜在攻击的AOR记录,并且灰名单中包含属于白名单和黑名单的AOR记录。 根据学习行为对消息的分析和检查,作出决定允许,拒绝,隔离或重定向消息。
    • 9. 发明申请
    • Interprocedural computing code optimization method and system
    • 程序间计算代码优化方法和系统
    • US20050044538A1
    • 2005-02-24
    • US10921004
    • 2004-08-17
    • Srinivas Mantripragada
    • Srinivas Mantripragada
    • G06F9/45
    • G06F8/443
    • A system for optimizing computing code containing procedures identifies code blocks as hot blocks or cold blocks in each procedure based on the local block weights of the code blocks in the procedure. The hot blocks are grouped into an intraprocedure hot section and an intraprocedure cold section for each procedure to optimize the procedure. The intraprocedure hot sections in the procedures are selectively grouped into an interprocedure hot section and the intraprocedure cold sections are selectively grouped into an interprocedure cold section, based on global block weights of the code blocks, to optimize the computing code. Additionally, code sections from called procedures can be duplicated into calling procedures to further optimize the computing code.
    • 用于优化包含过程的计算代码的系统基于过程中的代码块的本地块权重将代码块识别为每个过程中的热块或冷块。 热块被分组成一个进程内热段和一个进程内冷段,用于每个过程以优化程序。 过程中的进程内热点部分被选择性地分组为过程间热段,并且基于代码块的全局块权重,将进程内冷部分选择性地分组到过程间冷部分中,以优化计算代码。 另外,来自被调用过程的代码段可以被复制到调用过程中以进一步优化计算代码。