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MASCARA (ModulAr Semantic CAching fRAmework) towards FPGA acceleration for IoT Security monitoring

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Abstract

With the explosive growth of the Internet Of Things (IOTs), emergency security monitoring becomes essential to efficiently manage an enormous amount of information from heterogeneous systems. In concern of increasing the performance for the sequence of online queries on long-term historical data, query caching with semantic organization, called Semantic Query Caching or Semantic Caching (SC), can play a vital role. SC is implemented mostly in software perspective without providing a generic description of modules or cache services in the given context. Hardware acceleration with FPGA opens new research directions to achieve better performance for SC. Hence, our work aims to propose a flexible, adaptable, and tunable ModulAr Semantic CAching fRAmework (MASCARA) towards FPGA acceleration for fast and accurate massive logs processing applications.
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Dates and versions

hal-03017402 , version 1 (20-11-2020)

Identifiers

  • HAL Id : hal-03017402 , version 1

Cite

van Long Nguyen Huu, Julien Lallet, Emmanuel Casseau, Laurent d'Orazio. MASCARA (ModulAr Semantic CAching fRAmework) towards FPGA acceleration for IoT Security monitoring. VLIoT 2020 - International Workshop on Very Large Internet of Things, Sep 2020, Tokyo, Japan. pp.14-23. ⟨hal-03017402⟩
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