Distributed Data Processing Frameworks for Handling Large-Scale Cybersecurity Logs and Event Data

Abstract

The exponential growth of cybersecurity logs and event data poses significant challenges for data processing frameworks. Traditional approaches struggle to handle the volume, velocity, and variety of data in real-time. This paper explores distributed data processing frameworks designed to address these challenges, focusing on their architecture, performance, and suitability for large-scale cybersecurity applications. We evaluate frameworks such as Apache Hadoop, Apache Spark, and Apache Flink, and analyze their effectiveness in handling large-scale cybersecurity logs and event data.