TITLE:
Classification of Big Data Security Based on Ontology Web Language
AUTHORS:
Alsadig Mohammed Adam Abdallah, Amir Mohamed Talib
KEYWORDS:
Big Data, Big Data Security, Information Security, Data Security, Ontology Web Language, Protégé
JOURNAL NAME:
Journal of Information Security,
Vol.14 No.1,
January
31,
2023
ABSTRACT: A vast amount of data (known as big data) may now be collected and stored
from a variety of data sources, including event logs, the internet,
smartphones, databases, sensors, cloud computing, and Internet of Things (IoT)
devices. The term “big data security” refers to all the safeguards and
instruments used to protect both the data and analytics processes against
intrusions, theft, and other hostile actions that could endanger or adversely
influence them. Beyond being a high-value and desirable target, protecting Big
Data has particular difficulties. Big Data security does not fundamentally
differ from conventional data security. Big Data security issues are caused by
extraneous distinctions rather than fundamental ones. This study meticulously
outlines the numerous security difficulties Large Data analytics now faces and
encourages additional joint research for reducing both big data security
challenges utilizing Ontology Web Language (OWL). Although we focus on the
Security Challenges of Big Data in this essay, we will also briefly cover the
broader Challenges of Big Data. The proposed classification of Big Data
security based on ontology web language resulting from the protégé software has
32 classes and 45 subclasses.