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1. 发明申请

US20090074259A1 Automatic biometric identification based on face recognition and support vector machines 有权
标题翻译：基于人脸识别和支持向量机的自动生物识别
公开(公告)号：US20090074259A1
公开(公告)日：2009-03-19
申请号：US11989639
申请日：2005-07-29
申请人： Madalina Baltatu , Rosalia D'Alessandro , Roberta D'Amico , Massimo Tistarelli , Enrico Grosso , Manuele Bicego
发明人： Madalina Baltatu , Rosalia D'Alessandro , Roberta D'Amico , Massimo Tistarelli , Enrico Grosso , Manuele Bicego
IPC分类号： G06K9/00
CPC分类号： G06K9/00288 , G06K9/6284
摘要： An automatic biometric identification method based on face recognition and support vector machines, includes enrolling a user to generate a user's reference template; and identifying the user based on the user's reference template, wherein generating a user's reference template includes acquiring a number of user's face images, and training a one-class support vector machine based on the user's face images only.
摘要翻译：一种基于人脸识别和支持向量机的自动生物识别方法，包括登记用户生成用户参考模板; 以及基于所述用户的参考模板来识别所述用户，其中生成用户的参考模板包括获取用户的脸部图像的数量，以及仅基于所述用户的脸部图像来训练一类支持向量机。

2. 发明授权

US08275175B2 Automatic biometric identification based on face recognition and support vector machines 有权
标题翻译：基于人脸识别和支持向量机的自动生物识别
公开(公告)号：US08275175B2
公开(公告)日：2012-09-25
申请号：US11989639
申请日：2005-07-29
申请人： Madalina Baltatu , Rosalia D'Alessandro , Roberta D'Amico , Massimo Tistarelli , Enrico Grosso , Manuele Bicego
发明人： Madalina Baltatu , Rosalia D'Alessandro , Roberta D'Amico , Massimo Tistarelli , Enrico Grosso , Manuele Bicego
IPC分类号： G06K9/00
CPC分类号： G06K9/00288 , G06K9/6284
摘要： An automatic biometric identification method based on face recognition and support vector machines, includes enrolling a user to generate a user's reference template; and identifying the user based on the user's reference template, wherein generating a user's reference template includes acquiring a number of user's face images, and training a one-class support vector machine based on the user's face images only.
摘要翻译：一种基于人脸识别和支持向量机的自动生物识别方法，包括登记用户生成用户参考模板; 以及基于所述用户的参考模板来识别所述用户，其中生成用户的参考模板包括获取用户的脸部图像的数量，以及仅基于所述用户的脸部图像来训练一类支持向量机。

3. 发明授权

US08135180B2 User authentication method based on the utilization of biometric identification techniques and related architecture 有权
标题翻译：基于利用生物识别技术和相关架构的用户认证方法
公开(公告)号：US08135180B2
公开(公告)日：2012-03-13
申请号：US10584506
申请日：2004-12-10
申请人： Madalina Baltatu , Rosalia D'Alessandro , Roberta D'Amico
发明人： Madalina Baltatu , Rosalia D'Alessandro , Roberta D'Amico
IPC分类号： G06K9/00
CPC分类号： G07C9/00087 , G06F21/32 , G06F21/34 , G06K9/00973 , G07C9/00103 , H04L63/0442 , H04L63/0823 , H04L63/0861 , H04L63/12
摘要： A user authentication method based on the use of identification biometric techniques, including the steps of generating a reference biometric template from a first biometric image of a user to be authenticated; splitting the reference biometric template into a first and a second reference biometric template portion that can be physically separated; signing and enciphering the first and the second reference biometric template portion; storing the signed and enciphered first and second reference biometric template portion into different memories.
摘要翻译：一种基于识别生物识别技术的用户认证方法，包括以下步骤：从要认证的用户的第一生物图像生成参考生物特征模板; 将参考生物测定模板分成可物理分离的第一和第二参考生物测定模板部分; 签名并加密第一和第二参考生物特征模板部分; 将有符号和加密的第一和第二参考生物特征模板部分存储到不同的存储器中。

4. 发明申请

US20080019573A1 User Authentication Method Based On The Utilization Of Biometric Identification Techniques And Related Architecture 有权
标题翻译：基于生物特征识别技术和相关体系结构的用户认证方法
公开(公告)号：US20080019573A1
公开(公告)日：2008-01-24
申请号：US10584506
申请日：2004-12-10
申请人： Madalina Baltatu , Rosalia D'Alessandro , Roberta D'Amico
发明人： Madalina Baltatu , Rosalia D'Alessandro , Roberta D'Amico
IPC分类号： G06K9/00
CPC分类号： G07C9/00087 , G06F21/32 , G06F21/34 , G06K9/00973 , G07C9/00103 , H04L63/0442 , H04L63/0823 , H04L63/0861 , H04L63/12
摘要： A user authentication method based on the use of identification biometric techniques, including the steps of generating a reference biometric template from a first biometric image of a user to be authenticated; splitting the reference biometric template into a first and a second reference biometric template portion that can be physically separated; signing and enciphering the first and the second reference biometric template portion; storing the signed and enciphered first and second reference biometric template portion into different memories.
摘要翻译：一种基于识别生物识别技术的用户认证方法，包括以下步骤：从要认证的用户的第一生物图像生成参考生物特征模板; 将参考生物测定模板分成可物理分离的第一和第二参考生物测定模板部分; 签名并加密第一和第二参考生物特征模板部分; 将有符号和加密的第一和第二参考生物特征模板部分存储到不同的存储器中。

5. 发明申请

US20070107052A1 Method and apparatus for monitoring operation of processing systems, related network and computer program product therefor 审中-公开
标题翻译：用于监控处理系统，相关网络和计算机程序产品的操作的方法和装置
公开(公告)号：US20070107052A1
公开(公告)日：2007-05-10
申请号：US10582848
申请日：2003-12-17
申请人： Gianluca Cangini , Gerardo Lamastra , Francesco Coda Zabetta , Paolo Abeni , Madalina Baltatu , Rosalia D'Alessandro , Stefano Brusotti , Sebastiano Di Paola , Manuel Leone , Federico Frosali
发明人： Gianluca Cangini , Gerardo Lamastra , Francesco Coda Zabetta , Paolo Abeni , Madalina Baltatu , Rosalia D'Alessandro , Stefano Brusotti , Sebastiano Di Paola , Manuel Leone , Federico Frosali
IPC分类号： G06F12/14
CPC分类号： G06F21/554 , G06F21/55
摘要： Apparatus for monitoring operation of a processing system includes a set of modules for monitoring operation of a set of system primitives that allocate or release the system resources and are used by different processes running on the system. Preferably, the modules include at least one application knowledge module tracking the processes running on the system and monitoring the resources used thereby, a network knowledge module monitoring connections by the processes running on the system, a file-system analysis module monitoring the file-related operations performed within the system, and a device monitoring module monitoring operation of commonly used modules with the system. A preferred field of application is in host-based intrusion detection systems.
摘要翻译：用于监视处理系统的操作的装置包括一组模块，用于监视分配或释放系统资源并被系统上运行的不同进程使用的一组系统原语的操作。优选地，所述模块包括至少一个应用知识模块，其跟踪在系统上运行的进程并监视由此使用的资源，网络知识模块通过系统上运行的进程来监视连接，文件系统分析模块监视文件相关在系统内执行的操作，以及设备监控模块，监视系统中常用模块的操作。优选的应用领域是基于主机的入侵检测系统。

6. 发明申请

US20100017618A1 METHOD AND SYSTEM FOR BIOMETRIC AUTHENTICATION AND ENCRYPTION 有权
标题翻译：用于生物识别和加密的方法和系统
公开(公告)号：US20100017618A1
公开(公告)日：2010-01-21
申请号：US12448596
申请日：2006-12-28
申请人： Jovan Golic , Madalina Baltatu
发明人： Jovan Golic , Madalina Baltatu
IPC分类号： H04L9/32
CPC分类号： H04L9/0866 , G06K9/00288 , G07C9/00158 , H04L9/3231 , H04L2209/08 , H04L2209/34
摘要： A biometric user authentication method, includes enrolling a user based on user's biometric samples to generate user's reference data; and authenticating the user based on a user's live biometric sample and the user's reference data; wherein enrolling a user includes acquiring the user's biometric samples; extracting an enrollment feature vector from each user's biometric sample; computing a biometric reference template vector as a mean vector based on the enrollment feature vectors; computing a variation vector based on the enrollment feature vectors and the mean vector; randomly generating an enrollment secret vector; computing an enrollment code vector based on the enrollment secret vector and the variation vector; computing a difference vector as a wrap-around difference between the enrollment code vector and the mean vector; computing an error correction vector based on the enrollment secret vector to enable error correction during the user authentication phase according to a given error tolerance level, wherein the error correction vector is not computed if the error tolerance level is equal to zero; and storing the variation vector, the difference vector, and the error correction vector as a part of the user's reference data to be used during the user authentication phase.
摘要翻译：一种生物识别用户认证方法，包括基于用户的生物测定样本登记用户以产生用户参考数据; 以及基于用户的实时生物特征样本和用户的参考数据来认证用户; 其中登记用户包括获取所述用户的生物特征样本; 从每个用户的生物特征样本中提取注册特征向量; 基于所述登记特征向量计算生物特征参考模板向量作为平均向量; 基于注册特征向量和平均向量计算变量向量; 随机生成登记秘密向量; 基于登记秘密向量和变化向量计算登记码矢量; 计算差分矢量作为注册码矢量和平均矢量之间的环绕差; 基于所述登记秘密向量计算误差校正向量，以根据给定的误差容限级别在所述用户认证阶段期间进行纠错，其中如果所述误差容限等级为零，则不计算所述误差校正向量; 并将变化矢量，差分矢量和误差校正矢量存储为用户认证阶段期间要使用的用户参考数据的一部分。

7. 发明授权

US09094444B2 Anomaly detection for packet-based networks 有权
标题翻译：基于分组的网络异常检测
公开(公告)号：US09094444B2
公开(公告)日：2015-07-28
申请号：US13143062
申请日：2008-12-31
申请人： Madalina Baltatu , Paolo Abeni
发明人： Madalina Baltatu , Paolo Abeni
IPC分类号： H04L12/26 , H04L29/06
CPC分类号： H04L63/1425 , H04L43/00
摘要： Disclosed herein is an anomaly detection method for a packet-based network which includes several network resources, also called network-related software objects. The method includes monitoring the network resources of the packet-based network, ordering the monitored network resources according to a given ordering criterion, and detecting an anomaly in the packet-based network based on the ordered network resources. In particular, detecting an anomaly includes forming a detection feature vector based on the ordered network resources, and feeding the detection feature vector to a machine learning system configured to detect an anomaly in the packet-based network based on the detection feature vector. The detection feature vector includes detection feature items related to corresponding monitored network resources, and arranged in the detection feature vector depending on the ordering of the corresponding monitored network resources. Conveniently, the machine learning system is a one-class classifier, preferably a one-class Support Vector Machine (OC-SVM).
摘要翻译：本文公开了一种用于基于分组的网络的异常检测方法，其包括若干网络资源，也称为网络相关软件对象。该方法包括监视基于分组的网络的网络资源，根据给定的排序标准对监控的网络资源进行排序，并基于有序的网络资源检测基于分组的网络中的异常。特别地，检测异常包括基于有序网络资源形成检测特征向量，并且将检测特征向量馈送到被配置为基于检测特征向量来检测基于分组的网络中的异常的机器学习系统。检测特征向量包括与对应的被监视的网络资源相关的检测特征项，并根据对应的被监视的网络资源的顺序排列在检测特征向量中。方便的是，机器学习系统是一类分类器，最好是一类支持向量机（OC-SVM）。

8. 发明授权

US08312291B2 Method and system for biometric authentication and encryption 有权
标题翻译：用于生物识别和加密的方法和系统
公开(公告)号：US08312291B2
公开(公告)日：2012-11-13
申请号：US12448596
申请日：2006-12-28
申请人： Jovan Golic , Madalina Baltatu
发明人： Jovan Golic , Madalina Baltatu
IPC分类号： G06F21/00
CPC分类号： H04L9/0866 , G06K9/00288 , G07C9/00158 , H04L9/3231 , H04L2209/08 , H04L2209/34
摘要： A biometric user authentication method, includes enrolling a user based on user's biometric samples to generate user's reference data; and authenticating the user based on a user's live biometric sample and the user's reference data; wherein enrolling a user includes acquiring the user's biometric samples; extracting an enrollment feature vector from each user's biometric sample; computing a biometric reference template vector as a mean vector based on the enrollment feature vectors; computing a variation vector based on the enrollment feature vectors and the mean vector; randomly generating an enrollment secret vector; computing an enrollment code vector based on the enrollment secret vector and the variation vector; computing a difference vector as a wrap-around difference between the enrollment code vector and the mean vector; computing an error correction vector based on the enrollment secret vector to enable error correction during the user authentication phase according to a given error tolerance level, wherein the error correction vector is not computed if the error tolerance level is equal to zero; and storing the variation vector, the difference vector, and the error correction vector as a part of the user's reference data to be used during the user authentication phase.
摘要翻译：一种生物识别用户认证方法，包括基于用户的生物测定样本登记用户以产生用户参考数据; 以及基于用户的实时生物特征样本和用户的参考数据来认证用户; 其中登记用户包括获取所述用户的生物特征样本; 从每个用户的生物特征样本中提取注册特征向量; 基于所述登记特征向量计算生物特征参考模板向量作为平均向量; 基于注册特征向量和平均向量计算变量向量; 随机生成登记秘密向量; 基于登记秘密向量和变化向量计算登记码矢量; 计算差分矢量作为注册码矢量和平均矢量之间的环绕差; 基于所述登记秘密向量计算误差校正向量，以根据给定的误差容限级别在所述用户认证阶段期间进行纠错，其中如果所述误差容限等级为零，则不计算所述误差校正向量; 并将变化矢量，差分矢量和误差校正矢量存储为用户认证阶段期间要使用的用户参考数据的一部分。

9. 发明授权

US08626678B2 Anomaly detection for link-state routing protocols 有权
标题翻译：链路状态路由协议异常检测
公开(公告)号：US08626678B2
公开(公告)日：2014-01-07
申请号：US12811048
申请日：2007-12-28
申请人： Madalina Baltatu , Sebastiano Di Paola , Dario Lombardo
发明人： Madalina Baltatu , Sebastiano Di Paola , Dario Lombardo
IPC分类号： G06F15/18
CPC分类号： H04L63/1425 , G06K9/6284 , H04L45/02
摘要： Disclosed herein is an anomaly detection method for link-state routing protocols, a link-state routing protocol providing for link-state update (LSU) messages to be exchanged between nodes in a packet-based network, wherein each link-state update message includes link-state advertisement (LSA) message(s) each having a respective header. The method comprises monitoring the link-state advertisement messages exchanged in the network, extracting and forming respective feature vectors with the values in the fields of the headers of the monitored link-state advertisement messages, and detecting an anomaly related to routing based on the feature vectors. In particular, detecting an anomaly related to routing includes feeding the feature vectors to a machine learning system, conveniently a one-class classifier, preferably a one-class support vector machine (OC-SVM).
摘要翻译：本文公开了一种用于链路状态路由协议的异常检测方法，提供要在基于分组的网络中的节点之间交换的链路状态更新（LSU）消息的链路状态路由协议，其中每个链路状态更新消息包括链路状态广播（LSA）消息，每个消息具有相应的报头。该方法包括监视在网络中交换的链路状态通告消息，提取和形成各个特征向量，其中所述被监控链路状态通告消息的报头的字段中的值，以及基于特征检测与路由有关的异常向量特别地，检测与路由相关的异常包括将特征向量馈送到机器学习系统，方便地是一类分类器，优选地是一类支持向量机（OC-SVM）。

10. 发明申请

US20110267964A1 ANOMALY DETECTION FOR PACKET-BASED NETWORKS 有权
标题翻译：基于分组网络的异常检测
公开(公告)号：US20110267964A1
公开(公告)日：2011-11-03
申请号：US13143062
申请日：2008-12-31
申请人： Madalina Baltatu , Paolo Abeni
发明人： Madalina Baltatu , Paolo Abeni
IPC分类号： H04L12/26
CPC分类号： H04L63/1425 , H04L43/00
摘要： Disclosed herein is an anomaly detection method for a packet-based network which includes several network resources, also called network-related software objects. The method includes monitoring the network resources of the packet-based network, ordering the monitored network resources according to a given ordering criterion, and detecting an anomaly in the packet-based network based on the ordered network resources. In particular, detecting an anomaly includes forming a detection feature vector based on the ordered network resources, and feeding the detection feature vector to a machine learning system configured to detect an anomaly in the packet-based network based on the detection feature vector. The detection feature vector includes detection feature items related to corresponding monitored network resources, and arranged in the detection feature vector depending on the ordering of the corresponding monitored network resources. Conveniently, the machine learning system is a one-class classifier, preferably a one-class Support Vector Machine (OC-SVM).
摘要翻译：本文公开了一种用于基于分组的网络的异常检测方法，其包括若干网络资源，也称为网络相关软件对象。该方法包括监视基于分组的网络的网络资源，根据给定的排序标准对监控的网络资源进行排序，并基于有序的网络资源检测基于分组的网络中的异常。特别地，检测异常包括基于有序网络资源形成检测特征向量，并且将检测特征向量馈送到被配置为基于检测特征向量来检测基于分组的网络中的异常的机器学习系统。检测特征向量包括与对应的被监视的网络资源相关的检测特征项，并根据对应的被监视的网络资源的顺序排列在检测特征向量中。方便的是，机器学习系统是一类分类器，最好是一类支持向量机（OC-SVM）。

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