Next-Generation Intrusion Detection and Prevention System Performance in Distributed Big Data Network Security Architectures

Abstract

Big data systems are expanding to support the rapidly growing needs of massive scale data analytics. To safeguard user data, the design and placement of cybersecurity systems is also evolving as organizations to increase their big data portfolios. One of several challenges presented by these changes is benchmarking real-time big data systems that use different network security architectures. This work introduces an eightstep benchmark process to evaluate big data systems in varying architectural environments. The benchmark is tested on realtime big data systems running in perimeter-based and perimeterless network environments. Findings show that marginal I/O differences exist on distributed file systems between network architectures. However, during various types of cyber incidents such as distributed denial of service (DDoS) attacks, certain security architectures like zero trust require more system resources than perimeter-based architectures. Results illustrate the need to broaden research on optimal benchmarking and security approaches for massive scale distributed computing systems.