Elaris Computing Nexus
Blockchain Enabled Big Data Analytics Framework for Secure Cyber Defense
Elaris Computing Nexus
Received On : 12 February 2025
Revised On : 10 April 2025
Accepted On : 30 April 2025
Published On : 25 May 2025
Volume 01, 2025
Pages : 050-060
Abstract
The dynamic increase in the cyber threats in distributed computing environment has added pressure on the need to implement sophisticated and intelligent defense mechanisms. Conventional methods of cybersecurity are usually not scalable, transparent and real-time responsive in managing high volumes of heterogeneous data. To resolve these drawbacks, it is suggested to implement a Big Data Analytics Framework with the help of a Blockchain to secure cyber defense. The framework uses blockchain to guarantee integrity, provenance and tamper resistance of the data and big data analytics to process data at high throughput in order to detect anomalies, detect intrusions and generate threat intelligence. The framework incorporates advanced machine learning models to improve predictive analysis and false positives. Experimental analysis based on benchmark cybersecurity data sets proves that the system has 96.8 percent detection, decreased by 23 percent false positives, and accelerated the response time relative to the state-of-the-art big data-based security models. The empirical analysis of CICIDS 2017 and UNSW-NB15 datasets shows a 96.8% detection rate, 23 percent decrease in false positives, and 17 percent response time better than the state-of-the-art big data-based security models. Scalability analysis of CSE-CIC-IDS 2018 indicates that it can handle the number of 1.2 million events per second with low latency. These findings make the suggested framework a strong and scalable solution to the next-generation cyber defense systems.
Keywords
Big Data Analytics, Blockchain, Cybersecurity, Intrusion Detection, Anomaly Detection, Predictive Analytics, Threat Intelligence, Secure Framework.
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CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization: Alina Granwehr and Verena Hofer; Writing-Original Draft Preparation: Alina Granwehr; Visualization: Alina Granwehr and Verena Hofer; Investigation: Alina Granwehr; Writing- Reviewing and Editing: Alina Granwehr and Verena Hofer; All authors reviewed the results and approved the final version of the manuscript.
Acknowledgements
Author(s) thanks to University Mohamed Khider Biskra for research lab and equipment support.
Funding
No funding was received to assist with the preparation of this manuscript.
Ethics Declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of Data and Materials
The datasets used to support the findings of this study are publicly available.
The CICIDS 2017 dataset can be accessed at https://www.unb.ca/cic/datasets/ids-2017.html
The UNSW-NB15 dataset is available through the Australian Centre for Cyber Security at https://research.unsw.edu.au/ projects/ unsw-nb15-dataset.
The CSE-CIC-IDS 2018 dataset can be obtained from the Canadian Institute for Cybersecurity at https://www.unb.ca/ cic/datasets/ids-2018.html.
These resources are open-access and widely used in cybersecurity research to evaluate intrusion detection and prevention systems.
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Cite this Article
Alina Granwehr and Verena Hofer, “Blockchain Enabled Big Data Analytics Framework for Secure Cyber Defense”, Elaris Computing Nexus, pp. 050-060, 2025, doi: 10.65148/ECN/2025006.
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© 2025 Alina Granwehr and Verena Hofer. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.