Blockchain Enabled Big Data Analytics Framework for Secure Cyber Defense
Year: 2025
Type: article
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.
Source: Elaris Computing Nexus
DOI: 10.65148/ecn/2025006
Authors: Alina Granwehr, Verena Hofer
Institution: University of Biskra
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415568590