Dynamic Data security for Hadoop Systems using Fuzzy Adaptive Security Profiles (FASP)
| Autor: | Sathisha M. S, K.C. Ravishankar |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Artificial neural network
Computer science business.industry Distributed computing Dynamic data 020208 electrical & electronic engineering Big data Volume (computing) Data security 020207 software engineering 02 engineering and technology Fuzzy adaptive Fuzzy logic Parallel processing (DSP implementation) 0202 electrical engineering electronic engineering information engineering business |
| Zdroj: | 2019 1st International Conference on Advances in Information Technology (ICAIT). |
| Popis: | Hadoop is an efficient parallel processing platform with ability to handle large volumes of data termed as big data. Ability to handle large volume of data using parallel processing means has made Hadoop widely adapted by various academics and Industries. Hadoop was not designed with security considerations during initial times and as Hadoop got deployed in public clouds; its security vulnerabilities became important. Towards this end, a Fuzzy Adaptive Security Profiles (FASP) is proposed in this work. The FASP is able to provide dynamic security profiles adaptive to the security and processing requirements. The system ensures data security for the HDFS with machine learning based attack detection. |
| Databáze: | OpenAIRE |
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