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Dynamically correlating network terrain to organizational missions

October 23, 2017
  |
Conference Paper
Author:
Alexia Schulz
…
Published in:
Proc. NATO IST-153/RWS-21 Workshop on Cyber Resilience, 23-25 October 2017.
Topic:
automation
R&D area:
R&D group:

Summary

A precondition for assessing mission resilience in a cyber context is identifying which cyber assets support the mission. However, determining the asset dependencies of a mission is typically a manual process that is time consuming, labor intensive and error-prone. Automating the process of mapping between network assets and organizational missions is highly desirable but technically challenging because it is difficult to find an appropriate proxy within available cyber data for an asset's mission utilization. In this paper we discuss strategies to automate the processes of both breaking an organization into its constituent mission areas, and mapping those mission areas onto network assets, using a data-driven approach. We have implemented these strategies to mine network data at MIT Lincoln Laboratory, and provide examples. We also discuss examples of how such mission mapping tools can help an analyst to identify patterns and develop contextual insight that would otherwise have been obscure.
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Summary

A precondition for assessing mission resilience in a cyber context is identifying which cyber assets support the mission. However, determining the asset dependencies of a mission is typically a manual process that is time consuming, labor intensive and error-prone. Automating the process of mapping between network assets and organizational missions...

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Dynamically correlating network terrain to organizational missions

Visualization evaluation for cyber security: trends and future directions(1.22 MB)

November 10, 2014
  |
Conference Paper
Author:
Diane P. Staheli
…
Published in:
Proceedings of the Eleventh Workshop on Visualization for Cyber Security
Topic:
visualization
R&D area:
R&D group:

Summary

The Visualization for Cyber Security research community (VizSec) addresses longstanding challenges in cyber security by adapting and evaluating information visualization techniques with application to the cyber security domain. In this paper, we survey and categorize the evaluation metrics, components, and techniques that have been utilized in the past decade of VizSec research literature.
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Summary

The Visualization for Cyber Security research community (VizSec) addresses longstanding challenges in cyber security by adapting and evaluating information visualization techniques with application to the cyber security domain. In this paper, we survey and categorize the evaluation metrics, components, and techniques that have been utilized in the past decade of...

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Visualization evaluation for cyber security: trends and future directions

D4M 2.0 Schema: a general purpose high performance schema for the Accumulo database

September 10, 2013
  |
Conference Paper
Author:
Jeremy Kepner
…
Published in:
HPEC 2013: IEEE Conf. on High Performance Extreme Computing, 10-12 September 2013.
Topic:
supercomputing
R&D area:
R&D group:

Summary

Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed consistency database that is widely used for government applications. Obtaining the full benefits of Accumulo requires using novel schemas. The Dynamic Distributed Dimensional Data Model (D4M) [http://www.mit.edu.ezproxy.canberra.edu.au/~kepner/D4M] provides a uniform mathematical framework based on associative arrays that encompasses both traditional (i.e., SQL) and non-traditional databases. For non-traditional databases D4M naturally leads to a general purpose schema that can be used to fully index and rapidly query every unique string in a dataset. The D4M 2.0 Schema has been applied with little or no customization to cyber, bioinformatics, scientific citation, free text, and social media data. The D4M 2.0 Schema is simple, requires minimal parsing, and achieves the highest published Accumulo ingest rates. The benefits of the D4M 2.0 Schema are independent of the D4M interface. Any interface to Accumulo can achieve these benefits by using the D4M 2.0 Schema.
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Summary

Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed consistency database that is widely used for government applications. Obtaining the full benefits of Accumulo requires using...

READ MORE
D4M 2.0 Schema: a general purpose high performance schema for the Accumulo database

D4M 2.0 Schema: a general purpose high performance schema for the Accumulo database

September 10, 2013
  |
Conference Paper
Author:
Jeremy Kepner
…
Published in:
HPEC 2013: IEEE Conf. on High Performance Extreme Computing, 10-12 September 2013.
Topic:
big data
R&D area:
R&D group:

Summary

Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed consistency database that is widely used for government applications. Obtaining the full benefits of Accumulo requires using novel schemas. The Dynamic Distributed Dimensional Data Model (D4M) [http://www.mit.edu.ezproxy.canberra.edu.au/~kepner/D4M] provides a uniform mathematical framework based on associative arrays that encompasses both traditional (i.e., SQL) and non-traditional databases. For non-traditional databases D4M naturally leads to a general purpose schema that can be used to fully index and rapidly query every unique string in a dataset. The D4M 2.0 Schema has been applied with little or no customization to cyber, bioinformatics, scientific citation, free text, and social media data. The D4M 2.0 Schema is simple, requires minimal parsing, and achieves the highest published Accumulo ingest rates. The benefits of the D4M 2.0 Schema are independent of the D4M interface. Any interface to Accumulo can achieve these benefits by using the D4M 2.0 Schema.
READ LESS

Summary

Non-traditional, relaxed consistency, triple store databases are the backbone of many web companies (e.g., Google Big Table, Amazon Dynamo, and Facebook Cassandra). The Apache Accumulo database is a high performance open source relaxed consistency database that is widely used for government applications. Obtaining the full benefits of Accumulo requires using...

READ MORE
D4M 2.0 Schema: a general purpose high performance schema for the Accumulo database

Driving big data with big compute

September 10, 2012
  |
Conference Paper
Author:
Chansup Byun
…
Published in:
HPEC 2012: IEEE Conf. on High Performance Extreme Computing, 10-12 September 2012.
Topic:
supercomputing
R&D area:
R&D group:

Summary

Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the big data and big compute communities together is an active area of research. The LLGrid team has developed and deployed a number of technologies that aim to provide the best of both worlds. LLGrid MapReduce allows the map/reduce parallel programming model to be used quickly and efficiently in any language on any compute cluster. D4M (Dynamic Distributed Dimensional Data Model) provided a high level distributed arrays interface to the Apache Accumulo database. The accessibility of these technologies is assessed by measuring the effort to use these tools and is typically a few lines of code. The performance is assessed by measuring the insert rate into the Accumulo database. Using these tools a database insert rate of 4M inserts/second has been achieved on an 8 node cluster.
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Summary

Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the...

READ MORE
Driving big data with big compute

Driving big data with big compute

September 10, 2012
  |
Conference Paper
Author:
Chansup Byun
…
Published in:
HPEC 2012: IEEE Conf. on High Performance Extreme Computing, 10-12 September 2012.
Topic:
high performance computing
R&D area:
R&D group:

Summary

Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the big data and big compute communities together is an active area of research. The LLGrid team has developed and deployed a number of technologies that aim to provide the best of both worlds. LLGrid MapReduce allows the map/reduce parallel programming model to be used quickly and efficiently in any language on any compute cluster. D4M (Dynamic Distributed Dimensional Data Model) provided a high level distributed arrays interface to the Apache Accumulo database. The accessibility of these technologies is assessed by measuring the effort to use these tools and is typically a few lines of code. The performance is assessed by measuring the insert rate into the Accumulo database. Using these tools a database insert rate of 4M inserts/second has been achieved on an 8 node cluster.
READ LESS

Summary

Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the...

READ MORE
Driving big data with big compute
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