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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing S. 647 - 656
Hauptverfasser: Cong Liao, Squicciarini, Anna
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2015
Schlagworte:
Access control
Cloud computing
computation integrity
Containers
Distributed databases
logging
MapReduce
Monitoring
provenance
Yarn
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
DOI:10.1109/CCGrid.2015.16