Abstract
Malware detection and identification is important to protect an organization’s data and enable end-to-end monitoring of resources accessible by multiple users through Internet. Malicious users and Intruders usually try various methods to gain unauthorized access to data from remote locations. This paper proposes a model that helps in finding the malware characteristics by extracting features of the data provided. This model is also tested for unknown malware files generated using various available tools. This paper discusses the steps used in building an effective model, Model for Malware Detection (MMD) using EMBER dataset and Keras. The results obtained with model accuracy of 97.2% are presented.
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Kumari, V.V., Jani, S. (2023). An Effective Model for Malware Detection. In: Rao, B.N.K., Balasubramanian, R., Wang, SJ., Nayak, R. (eds) Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol 315. Springer, Singapore. https://doi.org/10.1007/978-981-19-4162-7_35
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DOI: https://doi.org/10.1007/978-981-19-4162-7_35
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