Abstract: Techniques are provided for email protection using email signatures based on signing tokens. One method comprises obtaining a signed email from an email sender. The signed email comprises a second hash value generated using a source version of the signed email embedded with a signing token of the email sender. A data record stores a first hash value, based on the source version, and the second hash value. The second hash value is obtained from the signed email and compared to: (i) a copy of the second hash value obtained from the data record and/or (ii) a comparison value generated using a regenerated version of the source version embedded with the signing token of the email sender. A delivery of the signed email to an email recipient may be based on a result of the comparison.

Abstract: Methods, apparatus, and processor-readable storage media for predicting user input device activity using machine learning techniques are provided herein. An example computer-implemented method includes obtaining data pertaining to at least one of input device-related movement and input device-related action, and associated with a user using an application at a first temporal instance; predicting at least one of one or more input device-related movements and one or more input device-related actions to be carried out, at a second temporal instance subsequent to the first temporal instance, in connection with the user using the application, by processing at least a portion of the obtained data using one or more machine learning techniques; and performing one or more automated actions based at least in part on the at least one of the one or more predicted input device-related movements and the one or more predicted input device-related actions.

Abstract: An apparatus comprises at least one processing device configured to maintain an execution queue data structure comprising tasks to be executed, the tasks comprising execution of server-side code for application services hosted by servers in an information technology infrastructure environment. The at least one processing device is also configured to determine hardware and software requirements for the tasks in the execution queue data structure, and to determine hardware and software resources available on client devices in the information technology infrastructure environment. The at least one processing device is further configured to offload execution of at least a subset of the tasks in the execution queue data structure from the servers to the client devices based at least in part on mapping the determined available hardware and software resources of the client devices with the determined hardware and software requirements for the tasks in the execution queue data structure.

Abstract: A method comprises analyzing, using one or more machine learning algorithms, parameters corresponding to at least one microservice operation processed by a first instance of a microservice, and predicting, based at least in part on the analyzing, whether the at least one microservice operation is anomalous. The first instance of the microservice is designated as being in an anomalous state responsive to predicting that the at least one microservice operation is anomalous. One or more requests for the microservice are routed to a second instance of the microservice responsive to the anomalous state designation. The method further comprises causing logging of information corresponding to operation of the second instance of the microservice to be enabled at a designated level of granularity.

Abstract: One example method includes detecting an operation of an input device, obtaining a clickstream associated with a user, using information from the clickstream, generating a prediction as to a next action by the user using the input device, and presenting the prediction to the user for possible selection by the user. Selection of the prediction by the user eliminates the need for the user to perform input device manipulations that would otherwise be required if the prediction were not selected.

Abstract: An example methodology includes, by a computing device, determining a micro frontend (MFE) on a page of an application to hibernate based on interaction metrics of the MFE and a hibernation policy and capturing a screenshot image of the MFE. The method also includes, by the computing device, saving information about a current state of the MFE in a manifest associated with the MFE and replacing the MFE on the page of the application with the screenshot image of the MFE to hibernate the MFE.

Abstract: A peripheral storage system and method provide a separate host device with zero-configuration physical storage that has essentially unlimited capacity. The system simultaneously receives power from, and communicates data with, the host device using a standard port such as USB or SATA. A microcontroller in the peripheral storage system executes a virtual file system for storing data in a local data store, and a network interface controller (NIC) communicates data to a remote (e.g. cloud) data store whenever a Wi-Fi or cellular network connection is available. Data may be sent and received using checksums and/or encryption. The system and method may analyze data access patterns of the host device to perform adaptive caching. The peripheral storage system may be embodied as an integral part of the host device, or as a separate component having the form of, for example, a USB flash drive.