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Abstract

We are presenting a highly-efficient, novel architecture (which we call FAST, or Forensic Analysis of Sensitive Traces) for high-performance big data forensics for heterogeneous systems (CPU and GPU-based). Our model uses a highly-compact storage format of the widely known Aho-Corasick algorithm [1], as well as a partial pruning mechanism to ensure the lowest possible memory footprint, while maximizing throughput performance. We are comparing our performance with classic methods used in data forensics and observe significant memory footprint improvements, as well as massive throughput improvements throughout all stages of big data processing.

Keywords

Data forensics Big data High performance computing Efficient storage Aho-corasick GPU processing 

Notes

Acknowledgment

This work was partially supported by the VI-SEEM H2020-EINFRA 675121 grant and InnoHPC Interreg - Danube Transnational Programme grant. The views expressed in this paper do not necessarily reflect those of the corresponding projects consortium members.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of Mathematics and Informatics, Computer Science DepartmentWest University of TimisoaraTimisoaraRomania

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