Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.

Abstract: Systems, methods, and computer-readable media are disclosed for systems and methods for parallelized forensic analysis using cloud-based servers. Example methods may include generating a first request for one or more notifications in a notification queue, where the one or more notifications include a first notification indicative of a first data input at a datastore, determining a first data type of the first data input, and generating a second notification indicative of the first data type. Some example methods may include determining that a first software component is subscribed to notifications for the first data type, sending the second notification to the first software component, determining a first output of the first software component, where the first output comprises a set of extracted data from the first data input, and sending the set of extracted data to the datastore.

Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.

Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.

Abstract: A corpus of documents (and other data objects) stored for an entity can be analyzed to determine one or more topics for each document. Elements of the documents can be analyzed to also assign a risk score. The types of topics and security elements, and the associated risk scores, can be learned and adapted over time using, for example, a topic model and random forest regressor. Activity with respect to the documents is monitored, and expected behavior for a user determined using a trained recurrent neural network. Ongoing user activity is processed to determine whether the activity excessively deviates from the expected user activity. The activity can also be compared against the activity of user peers to determine whether the activity is also anomalous among the user peer group. For anomalous activity, risk scores of the accessed documents can be analyzed to determine whether to generate an alert.

Abstract: Embodiments of the invention extend a network protocol that is limited to a local area network (LAN) and enable it to function across the Internet. A LAN protocol is limited to providing services or resources to users within the same LAN. Embodiments of the invention make it possible for those services and resources to be available to users beyond the LAN. Embodiments of the invention also concern establishing connectivity without discovery for local area network protocols.

Abstract: Embodiments of the invention extend a network protocol that is limited to a local area network (LAN) and enable it to function across the Internet. A LAN protocol is limited to providing services or resources to users within the same LAN. Embodiments of the invention make it possible for those services and resources to be available to users beyond the LAN. Embodiments of the invention also concern establishing connectivity without discovery for local area network protocols.

Abstract: An instant-on environment consists of components residing in a computer boot ROM and/or also on a mass storage device. Main components to the instant-on environment include a loader, EPG1, and EPG2. The loader is a module that is integrated into the computer boot ROM, based on an embedded OS, that functions to load other instant-on environment components. There are various methods of integrating the loader into boot firmware to optimize for different requirements. EPG1 is a first user screen that appears on the computer display within seconds after power-on, and from which the user can choose to launch one of the instant-on environment's applications or launch the primary OS. EPG2 is launched if the user chooses to launch an instant-on environment application. EPG2 is a Linux-based desktop environment that the user enters once he selects an application from EPG1.

Abstract: A client transitions between a suspended virtual machine (VM) state and an active VM state by employing a working set comprising a VM state file and a working set index file. The VM state file serializes each of the VM components that is saved to storage. The working set index file contains indirect information that records the offset, length, and region of various pieces of the VM state and contains a VM working set. The VM suspend state serialization is implemented as either a monolithic or an incremental process.

Abstract: An instant-on environment consists of components residing in a computer boot ROM and/or also on a mass storage device. Main components to the instant-on environment include a loader, EPG1, and EPG2. The loader is a module that is integrated into the computer boot ROM, based on an embedded OS, that functions to load other instant-on environment components. There are various methods of integrating the loader into boot firmware to optimize for different requirements. EPG1 is a first user screen that appears on the computer display within seconds after power-on, and from which the user can choose to launch one of the instant-on environment's applications or launch the primary OS. EPG2 is launched if the user chooses to launch an instant-on environment application. EPG2 is a Linux-based desktop environment that the user enters once he selects an application from EPG1.