Abstract: A method protects a computer asset by identifying a particular signature, which is software that causes a particular gateway to block an intrusion from reaching a particular computer asset, and installs the particular signature on the particular gateway, thus protecting the computer asset from the intrusion.

Abstract: A method protects a computer asset by identifying a particular signature, which is software that causes a particular gateway to block an intrusion from reaching a particular computer asset, and installs the particular signature on the particular gateway, thus protecting the computer asset from the intrusion.

Abstract: An example operation may include one or more of receiving storage requests endorsed by blockchain peers of a blockchain, selecting a group of the endorsed storage requests to be stored together and ordering the group of endorsed storage requests with respect to each other based on timestamps, encoding the group of ordered and endorsed storage requests into an image, and storing the encoded image within a data section of a block of the blockchain.

Abstract: A system and method for automatic remediation of non-compliance events are provided. In embodiments, a computer-implemented method includes: accessing a compliance profile and a remediation profile, wherein the compliance profile includes compliance data regarding rules for an enterprise and the remediation profile includes remediation data regarding remediation actions to address non-compliance with one or more of the rules; generating mapped data by mapping compliance data in the compliance profile to remediation data in the remediation profile; receiving non-compliance event data from a workload node in a network; extracting information from the non-compliance event data including the workload node associated with the event and a cause of event; determining a remediation action for the event based on the information and the mapped data; and invoking automatic performance of the remediation action at the workload node based on the determined remediation action.

Abstract: A system and method for automatic remediation of non-compliance events are provided. In embodiments, a computer-implemented method includes: accessing a compliance profile and a remediation profile, wherein the compliance profile includes compliance data regarding rules for an enterprise and the remediation profile includes remediation data regarding remediation actions to address non-compliance with one or more of the rules; generating mapped data by mapping compliance data in the compliance profile to remediation data in the remediation profile; receiving non-compliance event data from a workload node in a network; extracting information from the non-compliance event data including the workload node associated with the event and a cause of event; determining a remediation action for the event based on the information and the mapped data; and invoking automatic performance of the remediation action at the workload node based on the determined remediation action.

Abstract: A method, system and computer-usable medium are disclosed for providing a travel network infrastructure. A dedicated Li-F connection is provided for each physical space such as a seat, expected to accommodate a passenger in a travel network such as an airline. A passenger is authenticated according to a physical space occupied by the passenger. If authentication is successful, a secure Li-Fi channel is established for the passenger. The access point of the Li-Fi channel is only leveraged by the passenger and no other passengers.

Abstract: Methods and a system of classifying unrecognized logs in an environment. The method includes inputting a log unrecognized during event collection into a machine learning model and predicting, by the machine learning model, a log source type of the log to allow for normalization of the log. The method also includes producing, by the machine learning model, a confidence score relating to the source type prediction, determining the confidence score exceeds a predetermined threshold, and submitting the log for normalization based on the log source type prediction. The method can also include predicting, by the machine learning model, an event name relating to the log, producing, by the machine learning model, a second confidence score relating to the event name prediction, determining the second confidence score exceeds another predetermined threshold, and submitting the log for normalization based on the identified log source type and the predicted event name.

Abstract: A method protects a computer asset by identifying a particular signature, which is software that causes a particular gateway to block an intrusion from reaching a particular computer asset, and installs the particular signature on the particular gateway, thus protecting the computer asset from the intrusion.

Abstract: A method selectively installs a particular signature on a particular gateway based on the type of signature and the type of computer asset that is protected by that particular gateway. A system and/or analyst receives multiple signatures, where different signatures from the multiple signatures are specific for different types of computer assets. The system and/or analyst identifies and extracts a particular signature, from the multiple signatures, that will protect, if implemented on the appropriate gateway, a particular computer asset. The system and/or analyst identifies the appropriate gateway that protects the particular computer asset, and installs only the extracted particular signature from the multiple signatures on that appropriate gateway.

Abstract: Systems and techniques that facilitate automated health-check risk assessment of computing assets are provided. In various embodiments, a system can comprise a baseline component that can generate a baseline health-check risk score that corresponds to non-compliance of a computing asset with a stipulated control. In various aspects, the system can further comprise an adjustment component that can adjust the baseline health-check risk score based on a weakness factor of the stipulated control. In some cases, the weakness factor can be based on a magnitude by which a state of the computing asset deviates from the stipulated control. In various embodiments, the adjustment component can further adjust the baseline health-check risk score based on an environmental factor of the computing asset. In various cases, the environmental factor can be based on security mechanisms or security protocols associated with the computing asset.

Abstract: A system and method for automatic remediation of non-compliance events are provided. In embodiments, a computer-implemented method includes: accessing a compliance profile and a remediation profile, wherein the compliance profile includes compliance data regarding rules for an enterprise and the remediation profile includes remediation data regarding remediation actions to address non-compliance with one or more of the rules; generating mapped data by mapping compliance data in the compliance profile to remediation data in the remediation profile; receiving non-compliance event data from a workload node in a network; extracting information from the non-compliance event data including the workload node associated with the event and a cause of event; determining a remediation action for the event based on the information and the mapped data; and invoking automatic performance of the remediation action at the workload node based on the determined remediation action.

Abstract: A method selectively installs a particular signature on a particular gateway based on the type of signature and the type of computer asset that is protected by that particular gateway. A system and/or analyst receives multiple signatures, where different signatures from the multiple signatures are specific for different types of computer assets. The system and/or analyst identifies and extracts a particular signature, from the multiple signatures, that will protect, if implemented on the appropriate gateway, a particular computer asset. The system and/or analyst identifies the appropriate gateway that protects the particular computer asset, and installs only the extracted particular signature from the multiple signatures on that appropriate gateway.

Abstract: A method, system and computer-usable medium are disclosed for providing a travel network infrastructure. A dedicated Li-F connection is provided for each physical space such as a seat, expected to accommodate a passenger in a travel network such as an airline. A passenger is authenticated according to a physical space occupied by the passenger. If authentication is successful, a secure Li-Fi channel is established for the passenger. The access point of the Li-Fi channel is only leveraged by the passenger and no other passengers.

Abstract: An example operation may include one or more of receiving storage requests endorsed by blockchain peers of a blockchain, selecting a group of the endorsed storage requests to be stored together and ordering the group of endorsed storage requests with respect to each other based on timestamps, encoding the group of ordered and endorsed storage requests into an image, and storing the encoded image within a data section of a block of the blockchain.

Abstract: Provided are techniques for dynamic LIFI extenders routing. One or more geographic locations of a physical region are identified based on historical crowd density, historical network traffic, current crowd density, and current network traffic. For each of the one or more geographic locations and a plurality of mobile bots, a mobile bot of the plurality of mobile bots is sent to that geographic location at a specified time, where the mobile bot provides a dynamic Light Fidelity (LIFI) extender router that acts as a network access point, and users at that geographic location are associated with the mobile bot, where each of the users has a LIFI enabled mobile device that communicates with a network of devices by sending data to the dynamic LIFI extender router via LIFI.

Abstract: An article of manufacture includes a substrate, a first patterned layer deposited on the substrate in a first region, and a second patterned layer deposited on the substrate in a second region. The first patterned layer encodes first information in first symbols that are detectable by an optical sensor. The second patterned layer encodes second information in second symbols that are detectable by a non-optical sensor. The second information is distinct from the first information and the second region at least partially overlaps the first region.

Abstract: An article of manufacture includes a substrate, a first patterned layer deposited on the substrate in a first region, and a second patterned layer deposited on the substrate in a second region. The first patterned layer encodes first information in first symbols that are detectable by an optical sensor. The second patterned layer encodes second information in second symbols that are detectable by a non-optical sensor. The second information is distinct from the first information and the second region at least partially overlaps the first region.

Abstract: An article of manufacture includes a substrate, a first patterned layer deposited on the substrate in a first region, and a second patterned layer deposited on the substrate in a second region. The first patterned layer encodes first information in first symbols that are detectable by an optical sensor. The second patterned layer encodes second information in second symbols that are detectable by a non-optical sensor. The second information is distinct from the first information and the second region at least partially overlaps the first region.

Abstract: An article of manufacture includes a substrate, a first patterned layer deposited on the substrate in a first region, and a second patterned layer deposited on the substrate in a second region. The first patterned layer encodes first information in first symbols that are detectable by an optical sensor. The second patterned layer encodes second information in second symbols that are detectable by a non-optical sensor. The second information is distinct from the first information and the second region at least partially overlaps the first region.

Abstract: A method and system for managing a workflow using instant messages to determine the statuses of interdependent tasks. First and second tasks of the workflow are determined to be interdependent, whereby the first task is required to be completed before the second task is started. After receiving a first instant message (IM) that responds to an IM-based first query requesting a status of the first task, the first IM is determined to include key word(s) indicating that the first task is completed. An IM-based second query requesting a status of the second task is sent. After receiving a second IM that responds to the second query, the second IM is determined to include the key word(s) indicating that the second task is completed.