Towards Provenance-Based Anomaly Detection in MapReduce

MapReduce enables parallel and distributed processing of vast amount of data on a cluster of machines. However, such computing paradigm is subject to threats posed by malicious and cheating nodes or compromised user submitted code that could tamper data and computation since users maintain little co...

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Published in:2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing pp. 647 - 656
Main Authors: Cong Liao, Squicciarini, Anna
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2015
Subjects:
Access control
Cloud computing
computation integrity
Containers
Distributed databases
logging
MapReduce
Monitoring
provenance
Yarn
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Abstract MapReduce enables parallel and distributed processing of vast amount of data on a cluster of machines. However, such computing paradigm is subject to threats posed by malicious and cheating nodes or compromised user submitted code that could tamper data and computation since users maintain little control as the computation is carried out in a distributed fashion. In this paper, we focus on the analysis and detection of anomalies during the process of MapReduce computation. Accordingly, we develop a computational provenance system that captures provenance data related to MapReduce computation within the MapReduce framework in Hadoop. In particular, we identify a set of invariants against aggregated provenance information, which are later analyzed to uncover anomalies indicating possible tampering of data and computation. We conduct a series of experiments to show the efficiency and effectiveness of our proposed provenance system.
AbstractList MapReduce enables parallel and distributed processing of vast amount of data on a cluster of machines. However, such computing paradigm is subject to threats posed by malicious and cheating nodes or compromised user submitted code that could tamper data and computation since users maintain little control as the computation is carried out in a distributed fashion. In this paper, we focus on the analysis and detection of anomalies during the process of MapReduce computation. Accordingly, we develop a computational provenance system that captures provenance data related to MapReduce computation within the MapReduce framework in Hadoop. In particular, we identify a set of invariants against aggregated provenance information, which are later analyzed to uncover anomalies indicating possible tampering of data and computation. We conduct a series of experiments to show the efficiency and effectiveness of our proposed provenance system.
Author Cong Liao
Squicciarini, Anna
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  givenname: Anna
  surname: Squicciarini
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  email: acs20@psu.edu
  organization: Coll. of Inf. Sci. & Technol., Pennsylvania State Univ., University Park, PA, USA
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Snippet MapReduce enables parallel and distributed processing of vast amount of data on a cluster of machines. However, such computing paradigm is subject to threats...
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StartPage 647
SubjectTerms Access control
Cloud computing
computation integrity
Containers
Distributed databases
logging
MapReduce
Monitoring
provenance
Yarn
Title Towards Provenance-Based Anomaly Detection in MapReduce
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