International Business Machines Corporation

Matthew Elsner

Ilgen Banu Yuceer

Ariel Farkash

Patrick Ryan Wardrop

Jian Lin

Brian Robert Matthiesen

Igor Gokhman

David G Druker

Ronald B Williams

Michael Josiah Bolding

Yun Pan

Cheng-Ta Lee

Paul Sherwood Taylor

Yoshio Iida

Omar M Rahim

Matthew Paul Ouellette

Edward Y Kwong

The example embodiments are directed to a system and method for managing blockchain transaction processing. In an example, the method includes one or more of receiving a message transmitted from a client device, the message including a predefined structural format for processing by a service providing computing system, determining a type of the message and detecting one or more sensitive fields within the message based on the determined type of the message, anonymizing values of the one or more sensitive fields within the message while leaving the predefined structural format intact, and transmitting the anonymized message including the one or more anonymized values with the predefined structural format remaining intact to the service providing computing system. The system can anonymize data from a private network before it is transmitted to a public service.

Data sanitization system for public host platform

A tiered machine learning-based infrastructure comprises a first machine learning (ML) tier configured to execute within an enterprise network environment and that learns statistics for a set of use cases locally, and to alert deviations from the learned distributions. Use cases typically are independent from one another. A second machine learning tier executes external to the enterprise network environment and provides further learning support, e.g., by determining a correlation among multiple independent use cases that are running locally in the first tier. Preferably, the second tier executes in a cloud compute environment for scalability and performance.

Distributed machine learning for anomaly detection

A machine learning (ML)-based technique for user behavior analysis that detects when users deviate from expected behavior. A ML model is trained using training data derived from activity data from a first set of users. The model is refined in a computationally-efficient manner by identifying a second set of users that constitute a 'watch list.' At a given time, a differential data ingestion operation is then performed to incorporate data for the second set of users into the training data, while also pruning at least a portion of the data set corresponding to data associated with any user included in the first set but not in the second set. These operations update the training data used for the machine learning. The machine learning model is then refined based on the updated training data that incorporates the activity data ingested from the users identified in the watch list.

Adaptive object modeling and differential data ingestion for machine learning

A machine learning-based technique for user behavior analysis that detects when users deviate from expected behavior. In this approach, a set of user groups are provided, preferably based on information provided from a user registry. A set of training data for each of the set of user groups is then obtained, preferably by collecting security events generated for a collection of the users over a given time period (e.g., a last thirty (30) days). A machine learning system is then trained using the set of training data to produce a model that includes a set of clusters in user behavior model, wherein a cluster is a learned user group that corresponds to a defined user group. Once the model is built, it is used to identify users that deviate from their expected group behavior. In particular, the system compares a current behavior of a user against the model and flags anomalous behavior. The user behavior analysis may be implemented in a security platform, such as a SIEM.

Detection of user behavior deviation from defined user groups

A color graphic display having a read/write control system for a buffer memory therein. The invention provides Line-on-Line and Underpaint by way of a method which invleves reading the contents of a frame buffer storage location for which new pixel data is being provided, comparing those contents with data representing a display background characteristic or color, and if the result of the comparison is positive, storing the new pixel data in the frame buffer storage location. If the result of the comparison is negative, a selected data value different from the new pixel data is stored in the frame buffer storage location.

Memory read/write control system for color graphic display

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Dunwoody, GA, United States of America

Matthew Elsner