Big-data security management issues

2014 ◽  
Author(s):  
Marisa Paryasto ◽  
Andry Alamsyah ◽  
Budi Rahardjo ◽  
Kuspriyanto
Keyword(s):  
Big Data ◽  
Data Security ◽  
Security Management ◽  
Management Issues

Big Data Security Management

2015 ◽  
pp. 53-66
Author(s):  
Zaiyong Tang ◽  
Youqin Pan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Scale Up ◽  
Big Data Analytics ◽  
Security Management ◽  
Future Research ◽  
Maturity Model ◽  
Research Issues ◽  
Security Standards ◽  
Industry Trends

Big data is a buzzword today, and security of big data is a big concern. Traditional security standards and technologies cannot scale up to deliver reliable and effective security solutions in the big data environment. This chapter covers big data security management from concepts to real-world issues. By identifying and laying out the major challenges, industry trends, legal and regulatory environments, security principles, security management frameworks, security maturity model, big data analytics in solving security problems, current research results, and future research issues, this chapter provides researchers and practitioners with a timely reference and guidance in securing big data processing, management, and applications.

Big Data Security Management

Big Data ◽  
2016 ◽  
pp. 247-260
Author(s):  
Zaiyong Tang ◽  
Youqin Pan
Keyword(s):  
Big Data ◽  
Data Security ◽  
Scale Up ◽  
Big Data Analytics ◽  
Security Management ◽  
Future Research ◽  
Maturity Model ◽  
Research Issues ◽  
Security Standards ◽  
Industry Trends

Big data is a buzzword today, and security of big data is a big concern. Traditional security standards and technologies cannot scale up to deliver reliable and effective security solutions in the big data environment. This chapter covers big data security management from concepts to real-world issues. By identifying and laying out the major challenges, industry trends, legal and regulatory environments, security principles, security management frameworks, security maturity model, big data analytics in solving security problems, current research results, and future research issues, this chapter provides researchers and practitioners with a timely reference and guidance in securing big data processing, management, and applications.

Big Data Security Management Countermeasures in the Prevention and Control of Computer Network Crime

2022 ◽  
Vol 30 (7) ◽  
pp. 0-0
Keyword(s):  
Big Data ◽  
Data Security ◽  
Prevention And Control ◽  
Computer Network ◽  
Security Management ◽  
Computer Crime ◽  
Judicial Practice ◽  
Scene Investigation ◽  
And Control ◽  
Characteristic Measure

This paper aims to study the Countermeasures of big data security management in the prevention and control of computer network crime in the absence of relevant legislation and judicial practice. Starting from the concepts and definitions of computer crime and network crime, this paper puts forward the comparison matrix, investigation and statistics method and characteristic measure of computer crime. Through the methods of crime scene investigation, network investigation and network tracking, this paper studies the big data security management countermeasures in the prevention and control of computer network crime from the perspective of criminology. The experimental results show that the phenomenon of low age is serious, and the number of Teenagers Participating in network crime is on the rise. In all kinds of cases, criminals under the age of 35 account for more than 50%.

Big Data Security Challenges and Solution of Distributed Computing in Hadoop Environment: A Security Framework

2020 ◽  
Vol 13 (4) ◽  
pp. 790-797
Author(s):  
Gurjit Singh Bhathal ◽  
Amardeep Singh Dhiman
Keyword(s):  
Big Data ◽  
Data Security ◽  
Data Sets ◽  
Security Framework ◽  
Hadoop Distributed File System ◽  
Current Scenario ◽  
Hadoop Cluster ◽  
Ciphertext Policy ◽  
In Transit ◽  
Hadoop Framework

Background: In current scenario of internet, large amounts of data are generated and processed. Hadoop framework is widely used to store and process big data in a highly distributed manner. It is argued that Hadoop Framework is not mature enough to deal with the current cyberattacks on the data. Objective: The main objective of the proposed work is to provide a complete security approach comprising of authorisation and authentication for the user and the Hadoop cluster nodes and to secure the data at rest as well as in transit. Methods: The proposed algorithm uses Kerberos network authentication protocol for authorisation and authentication and to validate the users and the cluster nodes. The Ciphertext-Policy Attribute- Based Encryption (CP-ABE) is used for data at rest and data in transit. User encrypts the file with their own set of attributes and stores on Hadoop Distributed File System. Only intended users can decrypt that file with matching parameters. Results: The proposed algorithm was implemented with data sets of different sizes. The data was processed with and without encryption. The results show little difference in processing time. The performance was affected in range of 0.8% to 3.1%, which includes impact of other factors also, like system configuration, the number of parallel jobs running and virtual environment. Conclusion: The solutions available for handling the big data security problems faced in Hadoop framework are inefficient or incomplete. A complete security framework is proposed for Hadoop Environment. The solution is experimentally proven to have little effect on the performance of the system for datasets of different sizes.

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