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Quantitative evaluation of dynamic platform techniques as a defensive mechanism

September 17, 2014
  |
Conference Paper
Author:
Hamed Okhravi
…
Published in:
RAID 2014: 17th Int. Symp. on Research in Attacks, Intrusions, and Defenses, 17-19 September 2014.
Topic:
systems analysis
R&D area:
R&D group:

Summary

Cyber defenses based on dynamic platform techniques have been proposed as a way to make systems more resilient to attacks. These defenses change the properties of the platforms in order to make attacks more complicated. Unfortunately, little work has been done on measuring the effectiveness of these defenses. In this work, we first measure the protection provided by a dynamic platform technique on a testbed. The counter-intuitive results obtained from the testbed guide us in identifying and quantifying the major effects contributing to the protection in such a system. Based on the abstract effects, we develop a generalized model of dynamic platform techniques which can be used to quantify their effectiveness. To verify and validate out results, we simulate the generalized model and show that the testbed measurements and the simulations match with small amount of error. Finally, we enumerate a number of lessons learned in our work which can be applied to quantitative evaluation of other defensive techniques.
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Summary

Cyber defenses based on dynamic platform techniques have been proposed as a way to make systems more resilient to attacks. These defenses change the properties of the platforms in order to make attacks more complicated. Unfortunately, little work has been done on measuring the effectiveness of these defenses. In this...

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Quantitative evaluation of dynamic platform techniques as a defensive mechanism

Computing on masked data: a high performance method for improving big data veracity

September 9, 2014
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Conference Paper
Author:
Jeremy Kepner
…
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
Topic:
supercomputing
R&D area:
R&D group:

Summary

The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic techniques that ensure the veracity of data can have overheads that are too large to apply to big data. This work introduces a new technique called Computing on Masked Data (CMD), which improves data veracity by allowing computations to be performed directly on masked data and ensuring that only authorized recipients can unmask the data. Using the sparse linear algebra of associative arrays, CMD can be performed with significantly less overhead than other approaches while still supporting a wide range of linear algebraic operations on the masked data. Databases with strong support of sparse operations, such as SciDB or Apache Accumulo, are ideally suited to this technique. Examples are shown for the application of CMD to a complex DNA matching algorithm and to database operations over social media data.
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Summary

The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. Along with these standard three V's of big data, an emerging fourth "V" is veracity, which addresses the confidentiality, integrity, and availability of the data. Traditional cryptographic...

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Computing on masked data: a high performance method for improving big data veracity

A survey of cryptographic approaches to securing big-data analytics in the cloud

September 9, 2014
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Conference Paper
Author:
Sophia Yakoubov
…
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
Topic:
supercomputing
R&D area:
R&D group:

Summary

The growing demand for cloud computing motivates the need to study the security of data received, stored, processed, and transmitted by a cloud. In this paper, we present a framework for such a study. We introduce a cloud computing model that captures a rich class of big-data use-cases and allows reasoning about relevant threats and security goals. We then survey three cryptographic techniques - homomorphic encryption, verifiable computation, and multi-party computation - that can be used to achieve these goals. We describe the cryptographic techniques in the context of our cloud model and highlight the differences in performance cost associated with each.
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Summary

The growing demand for cloud computing motivates the need to study the security of data received, stored, processed, and transmitted by a cloud. In this paper, we present a framework for such a study. We introduce a cloud computing model that captures a rich class of big-data use-cases and allows...

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A survey of cryptographic approaches to securing big-data analytics in the cloud

A test-suite generator for database systems

September 9, 2014
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Conference Paper
Author:
Ariel T. Hamlin
…
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
Topic:
systems analysis
R&D area:
R&D group:

Summary

In this paper, we describe the SPAR Test Suite Generator (STSG), a new test-suite generator for SQL style database systems. This tool produced an entire test suite (data, queries, and ground-truth answers) as a unit and in response to a user's specification. Thus, database evaluators could use this tool to craft test suites for particular aspects of a specific database system. The inclusion of ground-truth answers in the produced test suite, furthermore, allowed this tool to support both benchmarking (at various scales) and correctness-checking in a repeatable way. Lastly, the test-suite generator of this document was extensively profiled and optimized, and was designed for test-time agility.
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Summary

In this paper, we describe the SPAR Test Suite Generator (STSG), a new test-suite generator for SQL style database systems. This tool produced an entire test suite (data, queries, and ground-truth answers) as a unit and in response to a user's specification. Thus, database evaluators could use this tool to...

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A test-suite generator for database systems

Big Data dimensional analysis

September 9, 2014
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Conference Paper
Author:
Vijay N. Gadepally
…
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
Topic:
high performance computing
R&D area:
R&D group:

Summary

The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. One of the main challenges associated with big data variety is automatically understanding the underlying structures and patterns of the data. Such an understanding is required as a pre-requisite to the application of advanced analytics to the data. Further, big data sets often contain anomalies and errors that are difficult to know a priori. Current approaches to understanding data structure are drawn from the traditional database ontology design. These approaches are effective, but often require too much human involvement to be effective for the volume, velocity and variety of data encountered by big data systems. Dimensional Data Analysis (DDA) is a proposed technique that allows big data analysts to quickly understand the overall structure of a big dataset, determine anomalies. DDA exploits structures that exist in a wide class of data to quickly determine the nature of the data and its statistical anomalies. DDA leverages existing schemas that are employed in big data databases today. This paper presents DDA, applies it to a number of data sets, and measures its performance. The overhead of DDA is low and can be applied to existing big data systems without greatly impacting their computing requirements.
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Summary

The ability to collect and analyze large amounts of data is a growing problem within the scientific community. The growing gap between data and users calls for innovative tools that address the challenges faced by big data volume, velocity and variety. One of the main challenges associated with big data...

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Big Data dimensional analysis

Achieving 100,000,000 database inserts per second using Accumulo and D4M

September 9, 2014
  |
Conference Paper
Author:
Jeremy Kepner
…
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
Topic:
big data
R&D area:
R&D group:

Summary

The Apache Accumulo database is an open source relaxed consistency database that is widely used for government applications. Accumulo is designed to deliver high performance on unstructured data such as graphs of network data. This paper tests the performance of Accumulo using data from the Graph500 benchmark. The Dynamic Distributed Dimensional Data Model (D4M) software is used to implement the benchmark on a 216-node cluster running the MIT SuperCloud software stack. A peak performance of over 100,000,000 database inserts per second was achieved which is 100x larger than the highest previously published value for any other database. The performance scales linearly with the number of ingest clients, number of database servers, and data size. The performance was achieved by adapting several supercomputing techniques to this application: distributed arrays, domain decomposition, adaptive load balancing, and single-program-multiple-data programming.
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Summary

The Apache Accumulo database is an open source relaxed consistency database that is widely used for government applications. Accumulo is designed to deliver high performance on unstructured data such as graphs of network data. This paper tests the performance of Accumulo using data from the Graph500 benchmark. The Dynamic Distributed...

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Achieving 100,000,000 database inserts per second using Accumulo and D4M

Genetic sequence matching using D4M big data approaches

September 9, 2014
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Conference Paper
Author:
Darrell O. Ricke
…
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
Topic:
high performance computing
R&D area:
R&D group:

Summary

Recent technological advances in Next Generation Sequencing tools have led to increasing speeds of DNA sample collection, preparation, and sequencing. One instrument can produce over 600 Gb of genetic sequence data in a single run. This creates new opportunities to efficiently handle the increasing workload. We propose a new method of fast genetic sequence analysis using the Dynamic Distributed Dimensional Data Model (D4M) - an associative array environment for MATLAB developed at MIT Lincoln Laboratory. Based on mathematical and statistical properties, the method leverages big data techniques and the implementation of an Apache Acculumo database to accelerate computations one-hundred fold over other methods. Comparisons of the D4M method with the current gold-standard for sequence analysis, BLAST, show the two are comparable in the alignments they find. This paper will present an overview of the D4M genetic sequence algorithm and statistical comparisons with BLAST.
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Summary

Recent technological advances in Next Generation Sequencing tools have led to increasing speeds of DNA sample collection, preparation, and sequencing. One instrument can produce over 600 Gb of genetic sequence data in a single run. This creates new opportunities to efficiently handle the increasing workload. We propose a new method...

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Genetic sequence matching using D4M big data approaches

Using 3D printing to visualize social media big data

July 23, 2014
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Journal Article
Author:
Zachary J. Weber
…
Published in:
HPEC 2014: IEEE Conf. on High Performance Extreme Computing, 9-11 September 2014.
Topic:
3D printing
R&D area:
R&D group:

Summary

Big data volume continues to grow at unprecedented rates. One of the key features that makes big data valuable is the promise to find unknown patterns or correlations that may be able to improve the quality of processes or systems. Unfortunately, with the exponential growth in data, users often have difficulty in visualizing the often-unstructured, non-homogeneous data coming from a variety of sources. The recent growth in popularity of 3D printing has ushered in a revolutionary way to interact with big data. Using a 3D printed mockup up a physical or notional environment, one can display data on the mockup to show real-time data patterns. In this poster and demonstration, we describe the process of 3D printing and demonstrate an application of displaying Twitter data on a 3D mockup of the Massachusetts Institute of Technology (MIT) campus, known as LuminoCity.
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Summary

Big data volume continues to grow at unprecedented rates. One of the key features that makes big data valuable is the promise to find unknown patterns or correlations that may be able to improve the quality of processes or systems. Unfortunately, with the exponential growth in data, users often have...

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Using 3D printing to visualize social media big data

Finding focus in the blur of moving-target techniques

April 1, 2014
  |
Journal Article
Author:
Hamed Okhravi
…
Published in:
IEEE Security and Privacy, Vol. 12, No. 2, March/April 2014, pp. 16-26.
Topic:
systems architecture
R&D area:
R&D group:

Summary

Moving-target (MT) techniques seek to randomize system components to reduce the likelihood of a successful attack, add dynamics to a system to reduce the lifetime of an attack, and diversify otherwise homogeneous collections of systems to limit the damage of a large-scale attack. In this article, we review the five dominant domains of MT techniques, consider the advantages and weaknesses of each, and make recommendations for future research.
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Summary

Moving-target (MT) techniques seek to randomize system components to reduce the likelihood of a successful attack, add dynamics to a system to reduce the lifetime of an attack, and diversify otherwise homogeneous collections of systems to limit the damage of a large-scale attack. In this article, we review the five...

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Finding focus in the blur of moving-target techniques

Authenticated broadcast with a partially compromised public-key infrastructure

February 1, 2014
  |
Journal Article
Author:
S. Dov Gordon
…
Published in:
Info. and Comput., Vol. 234, February 2014, pp. 17-25.
Topic:
cryptography
R&D area:
R&D group:

Summary

Given a public-key infrastructure (PKI) and digital signatures, it is possible to construct broadcast protocols tolerating any number of corrupted parties. Existing protocols, however, do not distinguish between corrupted parties who do not follow the protocol, and honest parties whose secret (signing) keys have been compromised but continue to behave honestly. We explore conditions under which it is possible to construct broadcast protocols that still provide the usual guarantees (i.e., validity/agreement) to the latter. Consider a network of n parties, where an adversary has compromised the secret keys of up to tc honest parties, where an adversary has compromised the secret keys of up to tc honest parties and, in addition, fully controls the behavior of up to ta other parties. We show that for any fixed tc>0 and any fixed ta, there exists an efficient protocol for broadcast if and only if 2 ta + min (ta, tc) < n. (When tc = 0, standard results imply feasibility for all ta < n.) We also show that if tc, ta are not fixed, but are only guaranteed to satisfy the above bound, then broadcast is impossible to achieve except for a few specific values of n; for these "exceptional" values of n, we demonstrate broadcast protocols. Taken together, our results give a complete characterization of this problem.
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Summary

Given a public-key infrastructure (PKI) and digital signatures, it is possible to construct broadcast protocols tolerating any number of corrupted parties. Existing protocols, however, do not distinguish between corrupted parties who do not follow the protocol, and honest parties whose secret (signing) keys have been compromised but continue to behave...

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Authenticated broadcast with a partially compromised public-key infrastructure
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