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Title: Advanced Data Structures for Improved Cyber Resilience and Awareness in Untrusted Environments: LDRD Report

Technical Report ·
DOI:https://doi.org/10.2172/1528756· OSTI ID:1528756
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [1];  [9];  [10];  [1];  [11];  [12];  [13]
  1. Stony Brook Univ., NY (United States). Dept. of Computer Science
  2. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Discrete Math & Optimization
  3. Rutgers Univ., Piscataway, NJ (United States). Dept. of Computer Science
  4. Orlando Magic, Orlando, FL (United States)
  5. VMWare Research, Palo Alto, CA (United States)
  6. Sandia National Lab. (SNL-CA), Livermore, CA (United States). Enterprise Cybersecurity
  7. Stony Brook Univ., NY (United States). Dept. of Applied Mathematics and Statistics
  8. IT Univ. of Copenhagen (Denmark). Computer Science Dept.
  9. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States). Center for Computing Research
  10. Stanford Univ., CA (United States). Computer Science Dept.
  11. Univ. of California, Santa Cruz, CA (United States)
  12. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Computer Science and Artificial Intelligence Lab.
  13. Intel Corporation, Santa Clara, CA (United States)
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This report summarizes the work performed under the project "Advanced Data Structures for Improved Cyber Resilience and Awareness in Untrusted Environments." The goal of the project was to design, analyze, and test new data structures for cybersecurity applications. We had two major thrusts: 1) using new/improved write-optimized data structures and/or algorithms to better man- age and analyze high-speed massive-volume cyberstreams, and 2) adding security features to data structures at minimum cost. Write optimization allows data structures to better use secondary memory to store and search a larger amount of useful information. Secondary memory is large compared to main memory, but data movement to and from secondary memory must be carefully managed to run quickly enough to keep up with fast streams. The first thrust included managing cyberstreams in parallel, both multi-threaded and distributed, and improving the benchmarking infrastructure for testing new streaming data structures. We considered both (near) real-time discovery of particular patterns, and improved logging for improved forensics. We considered two kinds of security-feature problem. The first was high-performance history- independent external-memory data structures. These provide certain protections to data if a disk is stolen. We also prove some trade-offs between speed and security in this setting. The second data-security problem is more secure data look-up in secret-shared data bases. This report summarizes the project's major accomplishments, with the background to under- stand these accomplishments. It gathers the abstracts and references for the six refereed publications that have appeared as part of this work. We summarize several accomplishments that will be submitted for publication. We then archive one piece of partial work that is not likely to be published in the near future: validation of history-independent data structure implementations.

Research Organization:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
DOE Contract Number:
AC04-94AL85000; NA0003525
OSTI ID:
1528756
Report Number(s):
SAND-2018-5404; 663261
Country of Publication:
United States
Language:
English

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