This article addresses the pressing need for accurate anomaly detection techniques, particularly for cybersecurity applications. We emphasize the effectiveness of ensemble and machine learning techniques, as well as the parallelizability of the Unite and Conquer approach, to improve the efficiency, speed, and accuracy of calculations. More precisely, we introduce a variant of an existing framework for its optimization by taking into account the asynchronicity of the communications and evaluate its large-scale performance on the Fugaku supercomputer. Our evaluation focuses on the detection rate and response time of expertise using extensive datasets, including the UNSW-NB15 dataset, in the cybersecurity domain. Additionally, we discuss the framework’s expanded functionality and its potential integration into existing Security Orchestration, Automation, and Response (SOAR) systems, thereby strengthening cyber threat detection and response capabilities.

Introducing the Scalable Anomaly Detection with UC2B framework, this paper addresses the critical task of identifying unusual patterns in data, crucial for effective cyber threat defense. By leveraging ensemble learning methods and the parallel processing capabilities of the Unite and Conquer approach, the framework demonstrates its proficiency in handling large datasets. It strives to offer computational efficiency, scalability, and high accuracy in real-world applications. Notably, this paper places special emphasis on the diversity of components and acknowledges their substantial influence on the overall framework functionality. It encompasses features such as fault tolerance, adaptability to various architectures, and efficient load balancing. Experimental validation on the Ruche Cluster within the realm of cybersecurity provides valuable insights into its potential in detecting anomalies.