Systematic Review: Malware Detection and Classification in Cybersecurity
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 learnin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7747 |
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| author | Sebastian Berrios Dante Leiva Bastian Olivares Héctor Allende-Cid Pamela Hermosilla |
| author_facet | Sebastian Berrios Dante Leiva Bastian Olivares Héctor Allende-Cid Pamela Hermosilla |
| author_sort | Sebastian Berrios |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0e3cbf88a386485980d12637348abefc |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-0e3cbf88a386485980d12637348abefc2025-08-20T02:45:43ZengMDPI AGApplied Sciences2076-34172025-07-011514774710.3390/app15147747Systematic Review: Malware Detection and Classification in CybersecuritySebastian Berrios0Dante Leiva1Bastian Olivares2Héctor Allende-Cid3Pamela Hermosilla4Escuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, ChileEscuela de Ingeniería Informática, Pontificia Universidad Católica de Valparaíso, Valparaíso 2340025, ChileMalicious 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.https://www.mdpi.com/2076-3417/15/14/7747malwarecybersecuritymachine learningdetectionclassification |
| spellingShingle | Sebastian Berrios Dante Leiva Bastian Olivares Héctor Allende-Cid Pamela Hermosilla Systematic Review: Malware Detection and Classification in Cybersecurity Applied Sciences malware cybersecurity machine learning detection classification |
| title | Systematic Review: Malware Detection and Classification in Cybersecurity |
| title_full | Systematic Review: Malware Detection and Classification in Cybersecurity |
| title_fullStr | Systematic Review: Malware Detection and Classification in Cybersecurity |
| title_full_unstemmed | Systematic Review: Malware Detection and Classification in Cybersecurity |
| title_short | Systematic Review: Malware Detection and Classification in Cybersecurity |
| title_sort | systematic review malware detection and classification in cybersecurity |
| topic | malware cybersecurity machine learning detection classification |
| url | https://www.mdpi.com/2076-3417/15/14/7747 |
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