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Updated: Oct 26, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

A Novel Hadoop Security Model for Addressing Malicious Collusive Workers.

Amr M Sauber¹, Ahmed Awad^2,3, Amr F Shawish¹

¹Faculty of Science, Menoufia University, Shibin Al Kawm, Egypt.

Computational Intelligence and Neuroscience

|July 26, 2021

Summary

This study introduces a novel Malicious Workers' Trap (MWT) to secure Hadoop big data processing in cloud environments. MWT effectively detects untrusted workers with minimal overhead, enhancing system security and usability.

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Area of Science:

Computer Science
Cloud Computing
Big Data Analytics

Background:

Hadoop is a distributed system for big data processing using MapReduce.
Existing security solutions for Hadoop in cloud environments face challenges like high overhead and delayed detection of malicious workers.

Purpose of the Study:

To propose a novel model to enhance the security of cloud environments against untrusted workers in Hadoop.
To develop a component to detect both non-collusive and collusive malicious workers.

Main Methods:

Development of a Malicious Workers' Trap (MWT) component integrated into the master node.
Implementation and performance analysis of the MWT model in a Hadoop cluster.

Main Results:

The proposed MWT model accurately detects malicious workers.
MWT demonstrates minor processing overhead compared to vanilla MapReduce and Verifiable MapReduce (V-MR).
The model effectively balances security and usability in Hadoop clusters.

Conclusions:

The MWT model offers an effective solution for detecting malicious workers in Hadoop cloud environments.
MWT provides a secure and usable framework for big data processing, addressing limitations of existing approaches.