A Parallel and Asynchronous Approach for Anomaly Detection

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.