International Business Machines Corporation

Kyndryl, Inc.

Cindy J Mullen

Jinho Hwang

Sai Zeng

Mauro Marzorati

Muhammed Fatih Bulut

Vugranam Chakravarthy Sreedhar

Milton H Hernandez

Karin Murthy

Lisa M Chavez

Virginia Mayo Policarpio

Virginia Mayo

Laura Murphy

Xinli Wang

Barbara K Smith

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.

Unattended authentication in HTTP using time-based one-time passwords

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.

Self-learning optimized patch orchestration

A system includes a memory that stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An assessment component assesses a computer network, and classifies the computer network relative to M network classifications stored in a repository, wherein M is an integer greater than one. A risk component determines risk of vulnerability subject to change impact regarding protection against a computer virus or cyber-attack based on historical information regarding vulnerability exposure and vulnerability remediation changes relative to the classification of the computer network. A recommendation component that generates recommendations and best action to mitigate risk and impact, and remediate the vulnerabilities based on the risk assessment and business priorities.

Method and system to manage risk of vulnerabilities and corresponding change actions to address malware threats

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Madison, WI, United States of America

Cindy J Mullen