International Business Machines Corporation

James Brent Peterson

Bradley E Harris

Moazzam Khan

Ian M Molloy

Cheng-Ta Lee

Sulakshan Vajipayajula

Michael Vu Le

Paul Coccoli

Burkhard Ringlein

Deepti Sachdev

Jeffrey Lawrence Douglass

Preeti Ravindra

An example operation may include one or more of dividing a data file into a plurality of data chunks, generating a randomness value for each data chunk based on one or more predefined randomness tests, and accumulating generated randomness values of the plurality of data chunks to generate an accumulated randomness value, detecting whether the data file is one or more of encrypted and compressed based on the accumulated randomness value and a predetermined threshold value, and storing information about the detection via a storage.

Randomness detection in network data

A method, system, and computer-usable medium for analyzing security data formatted in STIX&#x2122; format. Data related to actions performed by one or more users is captured. Individual tasks, such as analytics or extract, transform, load (ETL) tasks related to the captured data is created. Individual tasks are registered to a workflow for executing particular security threat or incident analysis. The workflow is executed and visualized to perform the security threat or incident analysis.

Analytics engine for data exploration and analytics

Mechanisms are provided to perform security incident disposition operations. A security incident is received that includes a security incident data structure comprising metadata describing properties of the security incident, and a corresponding security knowledge graph which includes nodes representing elements associated with the security incident and edges representing relationships between the nodes. The security incident data structure and security knowledge graph are processed to extract a set of security incident features corresponding to the security incident and input the extracted set of security incident features into a trained security incident machine learning model. The model generates a disposition classification output based on results of processing the extracted set of security incident features. The disposition classification output is output to the source of the security incident data structure.

Security incident disposition predictions based on cognitive evaluation of security knowledge graphs

A mechanism is provided in a network security subsystem in a virtual machine monitor for policy based load distribution among a plurality of packet processing units. Responsive to receiving a packet from a virtual machine, the network security subsystem compares the packet to rules in a load distribution policy in the network security subsystem. Responsive to the packet matching a rule in the load distribution policy, the network security subsystem identifies a packet processing unit list and an action in the matching rule. The network security subsystem distributes the packet to a selected packet processing unit from the packet processing unit list based on the action.

Policy-based load distribution between host-based packet processing units

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Atlanta, GA, United States of America

James Brent Peterson