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short-paper

Toward big data analysis to improve enterprise information security

Authors:

Ismail Hababeh,

Tamer AlajramiAuthors Info & Claims

MEDES '18: Proceedings of the 10th International Conference on Management of Digital EcoSystems

Pages 106 - 109

https://doi.org/10.1145/3281375.3281393

Published: 25 September 2018 Publication History

Abstract

In recent years, big data and cloud computing are considered key trends of modern computer technology. Extracting valuable information is the key purpose of analyzing big data that needs to be secured in order to avoid any potential risks. Most cloud systems applications contain sensitive data, such as; financial, legal and private information. Therefore, threats on such data may put cloud systems holding this data at high risk. The demand on securing cloud systems applications has been increasing rapidly; however, big data protection is still a challenge. This paper proposes a new methodology to protect big data during analysis by classifying data before any action such as moving, copying or processing take place. Big data files are classified according to the criticality level of their contents into three categories from the most to the least sensitive: restricted, confidential and public. Based on big data classification, the encryption algorithm AES 128 is applied on confidential big data, while the encryption algorithm AES 256 is applied on the restricted big data files. The experimental results show that our method enhances the performance of big data analysis systems and outperforms other approaches in the literature.

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Digital Library

Cited By

Acheampong DMeso PAgyemang ICudjoe J(2024)Bridging the Digital Divide: Securing Information and Computer Systems in an Unequal WorldImplications of Information and Digital Technologies for Development10.1007/978-3-031-66986-6_6(77-90)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-3-031-66986-6_6
Yakubu MJohnson JKropczynski JGyamfi E(2023)Security Protocols and Mechanisms for Sharing Near-Real-Time Environmental Data with Multiple Audiences During DisastersProceedings of the Future Technologies Conference (FTC) 2023, Volume 210.1007/978-3-031-47451-4_15(214-224)Online publication date: 1-Nov-2023
https://doi.org/10.1007/978-3-031-47451-4_15
Hababeh IGharaibeh ANofal SKhalil I(2019)An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data MobilityIEEE Access10.1109/ACCESS.2018.28900997(9153-9163)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2890099

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Published In

cover image ACM Other conferences

MEDES '18: Proceedings of the 10th International Conference on Management of Digital EcoSystems

September 2018

253 pages

ISBN:9781450356220

DOI:10.1145/3281375

Conference Chairs:
Richard Chbeir,
Hiroshi Ishikawa,
Program Chairs:
Kazutoshi Sumiya,
Kenji Hatano,
Mario Koeppen

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 September 2018

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MEDES '18

MEDES '18: The 10th International Conference on Management of Digital EcoSystems

September 25 - 28, 2018

Tokyo, Japan

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MEDES '18 Paper Acceptance Rate 29 of 77 submissions, 38%;

Overall Acceptance Rate 267 of 682 submissions, 39%

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Cited By

Acheampong DMeso PAgyemang ICudjoe J(2024)Bridging the Digital Divide: Securing Information and Computer Systems in an Unequal WorldImplications of Information and Digital Technologies for Development10.1007/978-3-031-66986-6_6(77-90)Online publication date: 1-Aug-2024
https://doi.org/10.1007/978-3-031-66986-6_6
Yakubu MJohnson JKropczynski JGyamfi E(2023)Security Protocols and Mechanisms for Sharing Near-Real-Time Environmental Data with Multiple Audiences During DisastersProceedings of the Future Technologies Conference (FTC) 2023, Volume 210.1007/978-3-031-47451-4_15(214-224)Online publication date: 1-Nov-2023
https://doi.org/10.1007/978-3-031-47451-4_15
Hababeh IGharaibeh ANofal SKhalil I(2019)An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data MobilityIEEE Access10.1109/ACCESS.2018.28900997(9153-9163)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2018.2890099

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