Sap Se

Jona Hassforther

Thorsten Menke

Kathrin Nos

Marco Rodeck

Thomas Kunz

Florian Chrosziel

Harish Mehta

Hartwig Seifert

Wei-Guo Peng

Eugen Pritzkau

Lin Luo

Rita Merkel

Nan Zhang

Omar-Alexander Al-Hujaj

Jens Baumgart

Volker Guzman

Hristina Dinkova

Anne Jacobi

Florian Kraemer

Thanh-Phong Lam

Stefan Rossmanith

Otto Boehrer

Frank Gales

Fritz Hoffmeister

Rene Zink

A selection of data types is defined from available log data for an evaluation of events associated with an entity. One or more evaluations associated with the entity are defined and reference data is generated from the selection of data types based on the one or more defined evaluations. The one or more evaluations are grouped into a pattern. A three dimensional (3D) score diversity diagram visualization is initialized for display in a graphical user interface, where a point representing the entity in the visualization is localized in 3D space at a coordinate based on two-dimensional (2D) coordinates in a 2D coordinate system of a centroid of the calculated area of a polygon placed to into the 2D coordinate system and defined by the values of each evaluation associated with the entity.

Anomaly detection in enterprise threat detection

A computer-implemented method generates a trigger registration for a selected triggering type. The generated trigger registration is stored in a triggering persistency. A received event from an event persistency is analyzed and data associated with the analyzed event is compared with the triggering persistency. Based on the comparison and using a pattern execution framework, an enterprise threat detection (ETD) pattern is processed to perform actions responsive to the received event.

Realtime triggering framework

One or more entities are selected for which logged Events are to be displayed in an Event Series Chart. One or more filters and a timeframe are selected. Events are fetched from one or more selected log files based on the one or more selected filters and the timeframe. The fetched Events are displayed in an Event Series Chart according to an associated timestamp and identification Event property value associated with each fetched Event.

Displaying a series of events along a time axis in enterprise threat detection

A Content Service executing in a cloud-computing-based Cloud Platform receives enterprise threat detection (ETD) Content transmitted from an ETD Content Development System (CDS) as a publication of the ETD Content from the ETD CDS. The received ETD Content is stored into a Content Management System (CMS). A determination is made of a registered Client ETD System for which the ETD Content is relevant. The ETD Content is published to the registered Client ETD System.

Distributing cloud-computing platform content to enterprise threat detection systems

A filter is selected from one or more filters defined for an ETD Network Graph. Events are fetched from the selected log files based on the selected filter and entities identified based on the fetched Events. Relationships are determined between the identified entities, and the determined relationships and identified entities are displayed in the ETD Network Graph. An identified entity is selected to filter data in an ETD Event Series Chart. An Event is selected in the ETD Event Series Chart to display Event Attributes in an Event Attribute Dialog. An Event Attribute is selected in the Event Attribute Dialog to filter Events in the ETD Event Series Chart.

Analysis of complex relationships among information technology security-relevant entities using a network graph

An enterprise threat detection (ETD) forensic workspace is established according to a particular timeframe and permitting defining a selection of data types from available log data for an evaluation of events associated with one or more entities. A chart is defined illustrating a graphical distribution of a particular data type in the forensic workspace. A snapshot associated with the chart is generated, the snapshot saving a copy of all data necessary to re-create the chart into an associated snapshot object. The snapshot is associated with a snapshot page for containing the snapshot and the snapshot page is saved within the ETD forensic workspace.

Snapshot of a forensic investigation for enterprise threat detection

Subnet information and location information is received from a database by a smart data streaming engine (SDS). A particular subnet of the subnet information is associated with a particular location of the location information by a globally unique location ID value. Log event data received in the SDS is normalized as normalized log event data. The normalized log event data is enriched with subnet and location information as enriched log event data and written into a log event persistence in the database. A subnet ID value retrieved from an enriched log event of the enriched log event data is used by an enterprise threat detection (ETD) system to determine a location associated with the enriched log event using a location ID value associated with the subnet ID.

Location enrichment in enterprise threat detection

A log processing job executing on a log producing computing system is initiated for processing log data associated with the log producing computing system. Log entries are determined to be available for processing. At least one instance of a Log Extractor Factory, Reader, and Transformation component are instantiated for reading and transforming the log data. Read log data is transformed into a common semantic format as transformed log data and transmitted in real-time to a Streaming Component for storage in an Enterprise Threat Detection (ETD) System. A recovery point is stored with a recovery timestamp indicating a next log entry in the log data to process.

Providing semantic connectivity between a java application server and enterprise threat detection system using a J2EE data

A path associated with a set of selected log data is defined. An indication is received on a graphical user interface (GUI) to generate a bubblegram associated with the path, wherein the bubblegram comprises one or more bubbles, each bubble representing a particular dimension associated with the selected path. The one or more bubbles are rendered on the GUI according to a performed ranking of the one or more bubbles. A bubble is selected to generate a filter for the path based on the dimension associated with the bubble. A subsequent bubblegram is rendered based on a narrowed set of the selected log data.

Visualization of data distributed in multiple dimensions

Data characterizing a plurality of data accesses of objects at one or more log points is received by a data access framework. Thereafter, data accesses requiring logging are identified and one or more corresponding pre-specified attributes are then logged. Related apparatus, systems, techniques and articles are also described.

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Heidelberg, Germany

Jona Hassforther