A new mechanism to ensure integrity for MapReduce in cloud computing

Bendahmane, Ahmed; Essaaidi, Mohamed; Moussaoui, Ahmed; Younes, Abdunnaser

doi:10.1109/icmcs.2012.6320295

Cited by 1 publication

(3 citation statements)

References 26 publications

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“…Detection approaches include grouping and weighing [ 13 ], replication-based detection [ 11 ], quiz and testing [ 11 ], voting [ 21 ], and multiple clouds [ 14 ].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Bendahmane et al [ 13 ] proposed a technique based on the weighted t-first voting method. The approach replicates each task so that replicas are executed over multiple nodes.…”

Section: Background and Related Workmentioning

confidence: 99%

“…In recent years, many researchers have worked in this area to increase the security of systems and cloud computing. In order to ensure the accuracy of the computation, several frameworks have been designed, including SecureMR [ 12 ], VIAF [ 11 ], weighted t-first voting [ 13 ], IntegrityMR [ 14 ], Accountable MR [ 15 ], TS-TRV [ 16 ], and Verifiable MapReduce (V-MR) [ 1 ]. The goal is to detect malicious workers and mark them out to guarantee the integrity of the results.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Hadoop Security Model for Addressing Malicious Collusive Workers

Sauber

Awad

Shawish

et al. 2021

Computational Intelligence and Neuroscience

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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. This 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.

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“…Detection approaches include grouping and weighing [ 13 ], replication-based detection [ 11 ], quiz and testing [ 11 ], voting [ 21 ], and multiple clouds [ 14 ].…”

Section: Background and Related Workmentioning

confidence: 99%

“…Bendahmane et al [ 13 ] proposed a technique based on the weighted t-first voting method. The approach replicates each task so that replicas are executed over multiple nodes.…”

Section: Background and Related Workmentioning

confidence: 99%