Abstract: An account protection service to prevent user login or other protected endpoint request abuse. In one embodiment, the service collects user recognition data, preferably for each login attempt (e.g. data about the connection, session, and other relevant context), and it constructs a true user profile for each such user over time, preferably using the recognition data from successful logins. The profile evolves as additional recognition data is collected from successful logins. The profile is a model of what the user “looks like” to the system. For a subsequent login attempt, the system then calculates a true user score. This score represents how well the current user recognition data matches the model represented by the true user profile. The user recognition service is used to drive policy decisions and enforcement capabilities. Preferably, user recognition works in association with bot detection in a combined solution.

Abstract: An account protection service to prevent user login or other protected endpoint request abuse. In one embodiment, the service collects user recognition data, preferably for each login attempt (e.g. data about the connection, session, and other relevant context), and it constructs a true user profile for each such user over time, preferably using the recognition data from successful logins. The profile evolves as additional recognition data is collected from successful logins. The profile is a model of what the user “looks like” to the system. For a subsequent login attempt, the system then calculates a true user score. This score represents how well the current user recognition data matches the model represented by the true user profile. The user recognition service is used to drive policy decisions and enforcement capabilities. Preferably, user recognition works in association with bot detection in a combined solution.

Abstract: An account protection service to prevent user login or other protected endpoint request abuse. In one embodiment, the service collects user recognition data, preferably for each login attempt (e.g. data about the connection, session, and other relevant context), and it constructs a true user profile for each such user over time, preferably using the recognition data from successful logins. The profile evolves as additional recognition data is collected from successful logins. The profile is a model of what the user “looks like” to the system. For a subsequent login attempt, the system then calculates a true user score. This score represents how well the current user recognition data matches the model represented by the true user profile. The user recognition service is used to drive policy decisions and enforcement capabilities. Preferably, user recognition works in association with bot detection in a combined solution.

Abstract: A virtual machine on a physical host computer provides controlled access to protected data by creating and storing a “stored system fingerprint” from stable system values (SSVs) as existing when creating the stored system fingerprint. The SSVs include virtual-machine-specific values that change upon cloning the virtual machine (VM) but do not change upon migration of the VM. Upon a request for access to the protected data, a current system fingerprint is calculated from the SSVs as existing when processing the request, the current system fingerprint is compared to the stored system fingerprint to determine whether there is a predetermined degree of matching, and the requested access to the protected data is permitted only if there is the predetermined degree of matching.

Abstract: A virtual machine computing platform uses a security virtual machine (SVM) in operational communications with a risk engine which has access to a database including stored patterns corresponding to patterns of filtered operational data that are expected to be generated during operation of the monitored virtual machine when malware is executing. The stored patterns may have been generated during preceding design and training phases. The SVM is operated to (1) receive raw operational data from a virtual machine monitor, the raw operational data obtained from file system operations and network operations of the monitored virtual machine; (2) apply rule-based filtering to the raw operational data to generate filtered operational data; and (3) in conjunction with the risk engine, perform a mathematical (e.g., Bayesian) analysis based on the filtered operational data and the stored patterns in the database to calculate a likelihood that the malware is executing in the monitored virtual machine.