A Review on The Malware Detection Systems
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| Formato: | Recurso digital |
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Zenodo
2023
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| _version_ | 1866901351346208768 |
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| author | L L N V S R K Sai Surya Miriyala Sai Teja A Basanth Reddy |
| author_facet | L L N V S R K Sai Surya Miriyala Sai Teja A Basanth Reddy |
| contents | Malware is a type of malware designed to compromise and exploit computer systems, posing a major threat to the digital ecosystem. As the volume and complexity of malware continue to increase, the need for effective malware detection becomes more important. This document provides an overview of malware detection, highlighting their key methods, techniques, and challenges. In this study, we explore the variety of malware detection methods, including signature-based detection based on predefined patterns and heuristic-based detection that identifies different behaviors. Additionally, we are exploring the use of advanced machine learning techniques such as deep learning and hybrid techniques to increase the accuracy of machine detection. The importance of behavior analysis and vulnerability detection in identifying previously unseen threats is also discussed. Although this system is very secure, problems persist in maintaining new signatures and dealing with zero-day issues. As the arms race between attackers and defenders continues, future research directions such as using artificial intelligence for adaptation and prevention became clear. Analyzing the strengths and limitations of existing systems, this article provides a deeper understanding of the changing field of malware detection, helping researchers and practitioners develop more cybersecurity strategies professionally. While these systems offer substantial protection, challenges persist in maintaining up-to-date signatures and addressing zero-day vulnerabilities. As the arms race between attackers and defenders continues, future research directions like leveraging artificial intelligence for adaptive and proactive defense mechanisms are highlighted. |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18279289 |
| institution | Zenodo |
| language | |
| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Review on The Malware Detection Systems L L N V S R K Sai Surya Miriyala Sai Teja A Basanth Reddy malware detection systems cyber security methods techniques threats machine learning behavior analysis signature-based detection anomaly detection and heuristics. Malware is a type of malware designed to compromise and exploit computer systems, posing a major threat to the digital ecosystem. As the volume and complexity of malware continue to increase, the need for effective malware detection becomes more important. This document provides an overview of malware detection, highlighting their key methods, techniques, and challenges. In this study, we explore the variety of malware detection methods, including signature-based detection based on predefined patterns and heuristic-based detection that identifies different behaviors. Additionally, we are exploring the use of advanced machine learning techniques such as deep learning and hybrid techniques to increase the accuracy of machine detection. The importance of behavior analysis and vulnerability detection in identifying previously unseen threats is also discussed. Although this system is very secure, problems persist in maintaining new signatures and dealing with zero-day issues. As the arms race between attackers and defenders continues, future research directions such as using artificial intelligence for adaptation and prevention became clear. Analyzing the strengths and limitations of existing systems, this article provides a deeper understanding of the changing field of malware detection, helping researchers and practitioners develop more cybersecurity strategies professionally. While these systems offer substantial protection, challenges persist in maintaining up-to-date signatures and addressing zero-day vulnerabilities. As the arms race between attackers and defenders continues, future research directions like leveraging artificial intelligence for adaptive and proactive defense mechanisms are highlighted. |
| title | A Review on The Malware Detection Systems |
| topic | malware detection systems cyber security methods techniques threats machine learning behavior analysis signature-based detection anomaly detection and heuristics. |
| url | https://doi.org/10.5281/zenodo.18279289 |