Abstract: In an approach to unattended authentication in HTTP using time-based one-time passwords, a request is received from a client for a Hypertext Transfer Protocol (HTTP) authentication on a server. A challenge is sent to the client, where the challenge includes a header that indicates that a Time-based One-time Password (TOTP) is to be used for the HTTP authentication. A first response is received from the client based on a first TOTP value and a shared secret, wherein the first response is encoded based on an encoding mechanism included in the header. Responsive to validating the first TOTP value and the shared secret from the client, the client is authenticated.

Abstract: In an approach to unattended authentication in HTTP using time-based one-time passwords, a request is received from a client for a Hypertext Transfer Protocol (HTTP) authentication on a server. A challenge is sent to the client, where the challenge includes a header that indicates that a Time-based One-time Password (TOTP) is to be used for the HTTP authentication. A first response is received from the client based on a first TOTP value and a shared secret, wherein the first response is encoded based on an encoding mechanism included in the header. Responsive to validating the first TOTP value and the shared secret from the client, the client is authenticated.

Abstract: A self-learning patch-orchestration system receives requests to install instances of two or more types of patches on sets of hardware or software components. The system retrieves information about past efforts to install the same types of patches, including historic failure rates of each type of patch and average durations of time required to successfully install each type of patch. The system identifies a set of candidate patch-orchestration plans, each of which specifies a different sequence in which to install the patches. The system uses the historical records to rank the plans based on the expected loss of scheduled installation time that would be caused by each plan's patch failures. The system selects as optimal the plan incurring the least amount of lost time and other adverse effects, and directs an orchestration engine or other downstream mechanisms to install the requested patches in accordance with the optimal orchestration plan.

Abstract: A self-learning patch-orchestration system receives requests to install instances of two or more types of patches on sets of hardware or software components. The system retrieves information about past efforts to install the same types of patches, including historic failure rates of each type of patch and average durations of time required to successfully install each type of patch. The system identifies a set of candidate patch-orchestration plans, each of which specifies a different sequence in which to install the patches. The system uses the historical records to rank the plans based on the expected loss of scheduled installation time that would be caused by each plan's patch failures. The system selects as optimal the plan incurring the least amount of lost time and other adverse effects, and directs an orchestration engine or other downstream mechanisms to install the requested patches in accordance with the optimal orchestration plan.