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    • 11. 发明申请
    • CONTEXTUAL MAPPING OF WEB-PAGES, AND GENERATION OF FRAUD-RELATEDNESS SCORE-VALUES
    • 网页的背景映射以及欺诈相关性分数的生成
    • US20160307201A1
    • 2016-10-20
    • US15194593
    • 2016-06-28
    • BioCatch Ltd.
    • Avi TurgemanOren Kedem
    • G06Q20/40H04L29/06
    • G06Q20/4016G06F21/31G06F21/316G06F21/32G06F21/552G06F21/554G06F2221/2133H04L63/08H04L63/1408H04L63/1441H04L2463/144
    • Devices, systems, and methods of contextual mapping of web-page elements and other User Interface elements, for the purpose of differentiating between fraudulent transactions and legitimate transactions, or for the purpose of distinguishing between a fraudulent user and a legitimate user. User Interface elements of a website or webpage or application or other computerized service, are contextually analyzed. A first User Interface element is assigned a low fraud-relatedness score-value, since user engagement with the first User Interface element does not create a security risk or a monetary exposure. A second, different, User Interface element is assigned a high fraud-relatedness score-value, since user engagement with the second User Interface element creates a security risk or a monetary exposure. The fraud-relatedness score-values are taken into account, together with user-specific behavioral characteristics, in order to determine whether to generate a possible-fraud notification, or as part of generating a possible-fraud score for a particular set-of-operations.
    • 为了区分欺诈性交易和合法交易,或为了区分欺诈用户和合法用户,网页元素和其他用户界面元素的上下文映射的设备,系统和方法。 上下文分析网站或网页或应用程序或其他计算机化服务的用户界面元素。 第一个用户界面元素被赋予低欺诈相关性得分值,因为用户与第一用户界面元素的参与不会产生安全风险或货币风险。 第二个不同的用户界面元素被分配了高欺诈相关性得分值,因为用户与第二用户界面元素的参与会产生安全风险或货币风险。 考虑到欺诈相关性得分值以及用户特定的行为特征,以便确定是否产生可能的欺诈通知,或者作为生成特定集合的可能欺诈评分的一部分, 操作。
    • 16. 发明授权
    • Method, device, and system of generating fraud-alerts for cyber-attacks
    • 为网络攻击产生欺诈警报的方法,设备和系统
    • US09552470B2
    • 2017-01-24
    • US14675765
    • 2015-04-01
    • BioCatch Ltd.
    • Avi TurgemanOren KedemUri Rivner
    • G06F21/00H04L29/06G06F21/32G06F21/55G06F3/041G06F21/31G06F21/57H04L12/24
    • G06F21/32G06F3/041G06F21/31G06F21/316G06F21/554G06F2221/2133H04L63/08H04L63/1408
    • Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. An end-user device (a desktop computer, a laptop computer, a smartphone, a tablet, or the like) interacts and communicates with a server of a computerized server (a banking website, an electronic commerce website, or the like). The interactions are monitored, tracked and logged. User Interface (UI) interferences are intentionally introduced to the communication session; and the server tracks the response or the reaction of the end-user to such communication interferences. The system determines whether the user is a legitimate human user; or a cyber-attacker posing as the legitimate human user. The system displays gauges indicating cyber fraud scores or cyber-attack threat-levels. The system extrapolates from observed fraud incidents and utilizes a rules engine to automatically search for similar fraud events and to automatically detect fraud events or cyber-attackers.
    • 检测用户身份的设备,系统和方法,区分计算机化服务的用户和检测网络攻击者。 最终用户设备(台式计算机,膝上型计算机,智能电话,平板电脑等)与计算机化服务器(银行网站,电子商务网站等)的服务器交互并进行通信。 互动被监控,跟踪和记录。 用户界面(UI)干扰被有意地引入通信会话; 并且服务器跟踪最终用户对这种通信干扰的响应或反应。 系统确定用户是否是合法的人类用户; 或构成合法用户的网络攻击者。 系统显示指示网络欺诈评分或网络攻击威胁级别的计量器。 系统从观察到的欺诈事件推断出来,并利用规则引擎自动搜索类似的欺诈事件,并自动检测欺诈事件或网络攻击者。