A Novel Hadoop Security Model for Addressing Malicious Collusive Workers
Date
2021
Journal Title
Journal ISSN
Volume Title
Publisher
ProQuest Central
Abstract
With the daily increase of data production and collection, Hadoop is a platform for processing big data on a distributed system. A
master node globally manages running jobs, whereas worker nodes process partitions of the data locally. Hadoop uses
MapReduce as an effective computing model. However, Hadoop experiences a high level of security vulnerability over hybrid
and public clouds. Specially, several workers can fake results without actually processing their portions of the data. Several
redundancy-based approaches have been proposed to counteract this risk. A replication mechanism is used to duplicate all or
some of the tasks over multiple workers (nodes). A drawback of such approaches is that they generate a high overhead over the
cluster. Additionally, malicious workers can behave well for a long period of time and attack later. *is paper presents a novel
model to enhance the security of the cloud environment against untrusted workers. A new component called malicious
workers’ trap (MWT) is developed to run on the master node to detect malicious (noncollusive and collusive) workers as they
convert and attack the system. An implementation to test the proposed model and to analyze the performance of the system
shows that the proposed model can accurately detect malicious workers with minor processing overhead compared to vanilla
MapReduce and Verifiable MapReduce (V-MR) model [1]. In addition, MWT maintains a balance between the security and
usability of the Hadoop cluster
Description
Keywords
Citation
Sauber, A.M. et al. (2021) “A Novel Hadoop Security Model for Addressing Malicious Collusive Workers,” Computational Intelligence and Neuroscience : CIN, 2021.