Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. A user utilizes a desktop computer, a laptop computer, a smartphone, a tablet, or other electronic device, to interact with a banking website or application, a retailer website or application, or other computerized service. Input-unit interactions are monitored, logged, and analyzed. Based on several types of analysis of the input-unit interactions, a score is generated to reflect fraud-relatedness or attack-relatedness of the input-unit interactions. Based on the score, the system estimates or determines whether the user is an attacker, and initiates attack-mitigation operations or fraud-mitigation operations.

Abstract: Methods and systems provide for approaching end-users, collecting their information as it relates to a specific suspect event or set of events, for security events, such as financial events and computer security events, and then applying an Artificial Intelligence (AI) model, to a given security event, to analyze the provided information, to investigate the event or set of events, for validation.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. A user utilizes a desktop computer, a laptop computer, a smartphone, a tablet, or other electronic device, to interact with a banking website or application, a retailer website or application, or other computerized service. Input-unit interactions are monitored, logged, and analyzed. Based on several types of analysis of the input-unit interactions, a score is generated to reflect fraud-relatedness or attack-relatedness of the input-unit interactions. Based on the score, the system estimates or determines whether the user is an attacker, and initiates attack-mitigation operations or fraud-mitigation operations.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. A user utilizes a desktop computer, a laptop computer, a smartphone, a tablet, or other electronic device, to interact with a banking website or application, a retailer website or application, or other computerized service. Input-unit interactions are monitored, logged, and analyzed. Based on several types of analysis of the input-unit interactions, a score is generated to reflect fraud-relatedness or attack-relatedness of the input-unit interactions. Based on the score, the system estimates or determines whether the user is an attacker, and initiates attach-mitigation operations or fraud-mitigation operations.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. A user utilizes a desktop computer, a laptop computer, a smartphone, a tablet, or other electronic device, to interact with a banking website or application, a retailer website or application, or other computerized service. Input-unit interactions are monitored, logged, and analyzed. Based on several types of analysis of the input-unit interactions, a score is generated to reflect fraud-relatedness or attack-relatedness of the input-unit interactions. Based on the score, the system estimates or determines whether the user is an attacker, and initiates attach-mitigation operations or fraud-mitigation operations.

Abstract: System, device, and method for behaviorally validated link analysis, session linking, transaction linking, transaction back-coloring, transaction forward-coloring, fraud detection, and fraud mitigation. A method includes: receiving an indicator of a seed transaction known to be fraudulent; selecting, from a database of transactions, multiple transactions that share at least one common property with the seed transaction; generating a list of candidate fraudulent transactions; filtering the candidate fraudulent transactions, by applying a transaction filtering rule that is based on one or more behavioral characteristics; and generating a filtered list of candidate fraudulent transactions.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. A user utilizes a desktop computer, a laptop computer, a smartphone, a tablet, or other electronic device, to interact with a banking website or application, a retailer website or application, or other computerized service. Input-unit interactions are monitored, logged, and analyzed. Based on several types of analysis of the input-unit interactions, a score is generated to reflect fraud-relatedness or attack-relatedness of the input-unit interactions. Based on the score, the system estimates or determines whether the user is an attacker, and initiates attach-mitigation operations or fraud-mitigation operations.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a possible attacker. A log-in process or a user-authentication process, is augmented or enriched by one or more incidental tasks, which force the user to perform additional on-screen interactions or input-unit interactions, which in turn enrich and augment the pool of user interactions from which the system extracts one or more user-specific features. The extracted user-specific features are used as part of the user authentication process, and are further used to differentiate among users.

Abstract: System, device, and method for behaviorally validated link analysis, session linking, transaction linking, transaction back-coloring, transaction forward-coloring, fraud detection, and fraud mitigation. A method includes: receiving an indicator of a seed transaction known to be fraudulent; selecting, from a database of transactions, multiple transactions that share at least one common property with the seed transaction; generating a list of candidate fraudulent transactions; filtering the candidate fraudulent transactions, by applying a transaction filtering rule that is based on one or more behavioral characteristics; and generating a filtered list of candidate fraudulent transactions.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a possible attacker. A log-in process or a user-authentication process, is augmented or enriched by one or more incidental tasks, which force the user to perform additional on-screen interactions or input-unit interactions, which in turn enrich and augment the pool of user interactions from which the system extracts one or more user-specific features. The extracted user-specific features are used as part of the user authentication process, and are further used to differentiate among users.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a possible attacker. A log-in process or a user-authentication process, is augmented or enriched by one or more incidental tasks, which force the user to perform additional on-screen interactions or input-unit interactions, which in turn enrich and augment the pool of user interactions from which the system extracts one or more user-specific features. The extracted user-specific features are used as part of the user authentication process, and are further used to differentiate among users.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. A user utilizes a desktop computer, a laptop computer, a smartphone, a tablet, or other electronic device, to interact with a banking website or application, a retailer website or application, or other computerized service. Input-unit interactions are monitored, logged, and analyzed. Based on several types of analysis of the input-unit interactions, a score is generated to reflect fraud-relatedness or attack-relatedness of the input-unit interactions. Based on the score, the system estimates or determines whether the user is an attacker, and initiates attach-mitigation operations or fraud-mitigation operations.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. An end-user device 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 or irregularities are introduced; and the server tracks the response or the reaction of the end-user to such interferences. The system determines whether the user is a legitimate user, or a cyber-attacker or automated script posing as the legitimate user. The system utilizes classification of users into classes or groups, to deduce or predict how a group-member would behave when accessing the service through a different type of device. The system identifies user-specific traits that are platform-independent and thus can be further monitored when the user switches from a first platform to a second platform.

Abstract: 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.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a possible attacker. A log-in process or a user-authentication process, is augmented or enriched by one or more incidental tasks, which force the user to perform additional on-screen interactions or input-unit interactions, which in turn enrich and augment the pool of user interactions from which the system extracts one or more user-specific features. The extracted user-specific features are used as part of the user authentication process, and are further used to differentiate among users.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a possible attacker. The methods include monitoring of user-side input-unit interactions, in general and in response to an interference introduced to user-interface elements. The monitored interactions are used for detecting an attacker that utilizes a remote access channel; for detecting a malicious automatic script, as well as malicious code injection; to identify a particular hardware assembly; to perform user segmentation or user characterization; to enable a visual login process with implicit two-factor authentication; to enable stochastic cryptography; and to detect that multiple users are utilizing the same subscription account.

Abstract: 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.

Abstract: 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.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a cyber-attacker. An end-user device 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 or irregularities are introduced; and the server tracks the response or the reaction of the end-user to such interferences. The system determines whether the user is a legitimate user, or a cyber-attacker or automated script posing as the legitimate user. The system utilizes classification of users into classes or groups, to deduce or predict how a group-member would behave when accessing the service through a different type of device. The system identifies user-specific traits that are platform-independent and thus can be further monitored when the user switches from a first platform to a second platform.

Abstract: Devices, systems, and methods of detecting user identity, differentiating between users of a computerized service, and detecting a possible attacker. The methods include monitoring of user-side input-unit interactions, in general and in response to an interference introduced to user-interface elements. The monitored interactions are used for detecting an attacker that utilizes a remote access channel; for detecting a malicious automatic script, as well as malicious code injection; to identify a particular hardware assembly; to perform user segmentation or user characterization; to enable a visual login process with implicit two-factor authentication; to enable stochastic cryptography; and to detect that multiple users are utilizing the same subscription account.