A Framework for Automated Big Data Analytics in Cybersecurity Threat Detection

Abstract

This research presents a novel framework designed to enhance cybersecurity through the integration of Big Data analytics, addressing the critical need for scalable and real-time threat detection in large-scale environments. Utilizing technologies such as Apache Kafka for efficient data ingestion, Apache Flink for stream processing, and advanced machine learning models like LSTM and Autoencoders, the framework offers robust anomaly detection capabilities. It also includes automated response mechanisms using SOAR and XDR systems, significantly improving response times and accuracy in threat mitigation. The proposed solution not only addresses current challenges in handling vast and complex data but also paves the way for future advancements, such as the integration of more sophisticated AI techniques and application across various domains, including IoT and cloud security. This research contributes to the field by providing a comprehensive, adaptive, and scalable framework that meets the demands of modern cybersecurity landscapes.

Reason for Expression of Concern:

The Editors wish to alert readers to potential concerns regarding the reliability of the findings reported in “A Framework for Automated Big Data Analytics in Cybersecurity Threat Detection”.  The journal has initiated an additional editorial assessment of the article’s methodology, data provenance, and reported outcomes to confirm their reliability and reproducibility.

This notice is issued to ensure transparency while the review is ongoing. The Expression of Concern does not constitute a final determination regarding the validity of the work. The journal will update readers once the assessment is completed and will take any necessary editorial action in accordance with the journal’s policies and COPE guidance.

See expression of concern available at:

https://doi.org/10.58496/MJCS/2026/004

https://mesopotamian.press/journals/index.php/bigdata/article/view/1051