Abstract

Malicious Software, commonly known as Malware, represents a persistent threat to cybersecurity, targeting the confidentiality, integrity, and availability of information systems. The digital era, marked by the proliferation of connected devices, cloud services, and the advancement of machine learning, has brought numerous benefits; however, it has also exacerbated exposure to cyber threats, affecting both individuals and corporations. This systematic review, which follows the PRISMA 2020 framework, aims to analyze current trends and new methods for malware detection and classification. The review was conducted using data from Web of Science and Scopus, covering publications from 2020 and 2024, with over 47 key studies selected for in-depth analysis based on relevance, empirical results and citation metrics. These studies cover a variety of detection techniques, including machine learning, deep learning and hybrid models, with a focus on feature extraction, malware behavior analysis and the application of advanced algorithms to improve detection accuracy. The results highlight important advances, such as the improved performance of ensemble learning and deep learning models in detecting sophisticated threats. Finally, this study identifies the main challenges and outlines opportunities of future research to improve malware detection and classification frameworks.