Ensembling Methods for Data Privacy in Data Science

Abstract

The rapid advancement of technology has unified systems, data storage, applications, and operations, providing continuous services to organizations. However, this integration also introduces new vulnerabilities, particularly the risk of cyber-attacks. Malware and digital piracy pose significant threats to data security, with the potential to compromise sensitive information, leading to severe financial and reputational damage. This study aims to develop an effective method for detecting malware-infected files on storage devices within the Internet of Things (IoT) environment. The proposed approach utilizes a stacked regression ensemble for data pre-processing and the Sea Lion Optimization Algorithm (sLOA) to extract salient features, enhancing the classification process. Using malware data from an intrusion detection dataset, an ensemble classification technique is applied to identify malicious infections. The experimental results demonstrate that the proposed method achieved an accuracy of 98%, a precision of 99.6%, a recall of 96%, and an F-measure of 95% by the final iteration, significantly outperforming existing techniques in addressing cyber-security challenges within IoT systems.