International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Review on Ensemble Based Learning for Malware Detection: A Review

Author(s) PARAMJEET KAUR, VIJAY LAXMI
Country India
Abstract Malware analysis becomes one of the main issues in today's world since attackers are producing a variety of malwares, and even their characteristics are updating at an incredibly fast rate. Malware has to be discovered before it impacts a lot of systems to safeguard computer systems and the internet from them. Several types of research on malware detection techniques have recently been conducted. But it is still difficult to identify malware. For identifying malware, there are essentially two methods: One is an identification method that is based on signatures, and the other is dependent on behaviour. The behaviour-based strategy may detect new and complicated malware to some degree utilizing machine learning and other techniques, but it is a difficult one. Signature-based is rapid and effective just for detecting known malware. In this paper, various techniques of malware detection are reviewed and analysed in terms of certain parameters.
Keywords Malware detection, Machine Learning, Feature extraction, Classification
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 5, September-October 2024
Published On 2024-10-30
Cite This Review on Ensemble Based Learning for Malware Detection: A Review - PARAMJEET KAUR, VIJAY LAXMI - IJFMR Volume 6, Issue 5, September-October 2024. DOI 10.36948/ijfmr.2024.v06i05.29627
DOI https://doi.org/10.36948/ijfmr.2024.v06i05.29627
Short DOI https://doi.org/g8p2td
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