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.
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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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Pungila, C., Negru, V. (2018). FAST: A High-Performance Architecture for Heterogeneous Big Data Forensics. In: Pérez García, H., Alfonso-Cendón, J., Sánchez González, L., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding. SOCO ICEUTE CISIS 2017 2017 2017. Advances in Intelligent Systems and Computing, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-319-67180-2_60
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DOI: https://doi.org/10.1007/978-3-319-67180-2_60
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