Harnessing Machine Learning for Real-Time Cybersecurity: A Scalable Approach Using Big Data Frameworks

The ever-evolving landscape of cyber threats demands innovative and scalable solutions to ensure robust real-time protection of digital infrastructures. This paper explores the integration of machine learning (ML) with big data frameworks to address the challenges of real-time cybersecurity. Traditional approaches often struggle to keep pace with the sheer volume, velocity, and variety of modern cybersecurity data, leading to delays in threat detection and increased vulnerability to sophisticated attacks. By leveraging ML algorithms, such as anomaly detection, supervised and unsupervised learning, and ensemble methods, alongside distributed big data technologies like Apache Spark and Hadoop, this research proposes a scalable framework for real-time threat analysis.

The paper outlines the limitations of existing systems, including high rates of false positives and difficulty in handling multi-vector attacks, and demonstrates how ML models can enhance accuracy and efficiency. The integration of big data platforms facilitates parallel processing of large datasets, enabling real-time insights into network traffic, user behavior, and anomaly detection.

The research evaluates various ML models and big data frameworks, comparing their performance based on detection rates, processing speed, and resource efficiency. Results indicate that combining ML with distributed computing significantly improves scalability and responsiveness in cybersecurity systems. Graphical and tabular analyses highlight the strengths of this approach, offering actionable insights for enterprises aiming to fortify their defenses.

The study concludes by discussing future opportunities, such as employing advanced deep learning models and ensuring ethical implementation in cybersecurity operations. This work provides a comprehensive foundation for scalable, real-time cybersecurity systems, bridging the gap between traditional defenses and the demands of the digital age.