Detection Of Dynamic Vulnerabilites In Hadoop Systems For Controlling The Fuzzy Adaptive Security Profiles (FASP)

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ATHISHA M.S, K.C. RAVISHANKAR

Abstract

Hadoop is big data processing framework with capability to process large volumes of data using map reduce parallel processing paradigm. Big data analytics on these large volumes of information provides various intelligent information for business process optimization and governance. With wide acceptance of Hadoop for big data analytics, there is also an increased security vulnerability. In our earlier work [1], Fuzzy Adaptive Security Profile (FASP) was proposed to provide increased security to Hadoop processing platform. The work has shortcoming in terms of protection against wide variety of security vulnerabilities. The approach considered only denial of service attack. Common vulnerability Exposure (CVE) has detailed 23 different vulnerabilities in Hadoop and this work designs a vulnerability scanner based on Hidden Markov model to detect the CVE attacks specific to Hadoop.  The designed vulnerability scanner is integrated with FASP using an adaptive security scoring technique to trigger adaptive mitigation mechanisms.

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