International Business Machines Corporation

Kyndryl, Inc.

Marcel Butucea Panait

Michal S Paluch

Erik Rueger

Szymon Kowalczyk

Nicolo' Sgobba

Jiri Barak

Jiri Grunseisen

Oleksii Tosov

A computer-implemented method obtains information about an external computer system having a connection to a computer system. The method may comprise receiving a request from the external computer system. In response to categorizing the external computer system as a potential threat responsive to the request, a computer file is generated responsive to the request. The computer file comprises instructions which are designed for obtaining the information about the external computer system if the instructions are executed on a processor of the external computer system. Furthermore, data is provided responsive to the request. The data is sent together with the computer file to the external computer system. The information about the external computer system, which is generated responsive to the instructions of the computer file, is received by the computer system for preserving it for a later use for developing measures for protecting the computer system.

Tracking a potential attacker on an external computer system

A method is provided for rate limiting in a network. The method comprises receiving a traffic flow from the network. In a supervised learning phase, and determining if the traffic flow matches a pretrained network flow model. If so, the method comprises designating the traffic flow as a classified traffic flow according to the pretrained network flow model. The method further comprises advancing to a grouping phase, conditioned upon the traffic flow not matching pretrained network flow models. In the unsupervised learning phase, the method comprises designating the traffic flow as a classified traffic flow. In the grouping phase that follows the supervised learning phase and the unsupervised learning phase, the method comprises using side information about the traffic flows to assign related traffic flows into traffic flow groups, identifying a particular traffic flow group from the traffic flow groups as being an offending traffic flow group.

Adaptive network control of traffic flows in a secure network

Generating a data protection and recovery data backup option by identifying static and runtime metadata for a computing application, determining application criticality of the computing application according to the static metadata using a first machine learning model, determining a data backup option for the computing application according to application criticality and the runtime metadata, using a second machine learning model.

Data protection and recovery

One or more computer processors install a trigger on a primary database. The one or more computer processors, responsive to the trigger activating and a data modification associated with the primary database, format the data modification into a universal format for a plurality of backup databases. The one or more computer processors rotate an active backup database from the plurality of backup databases based on a determined alternating backup period. The one or more computer processors synchronize in real-time the primary database with the active backup database.

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Brno, Czechia

Marcel Butucea Panait