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- W4383670021 abstract "Nowadays, the security of systems is one of the prime focuses of any organization. Malware detection techniques can be in static form or dynamically identify malware present inside network communications that affect the performance of systems. The malware detection is the field of network security where different techniques for detecting malicious software are used to protect a network from any harmful consequences. This paper presents a method for detecting malware using machine learning classifiers. The proposed methodology deploys six machine learning classifiers over three publicly available authentic datasets. Around 1.2 million features of malware are extracted from these datasets using feature engineering. The static, dynamic, hybrid, and URL features are also considered in the proposed methodology. The performance of classifiers is compared in terms of detection rate and false positive rate. The SVM classifier with hyper-parameter 0.01 and threshold of 0.02 was found more accurate among the six classifiers. The proposed work provides insights into large networked systems by classifying software as malicious or benign." @default.
- W4383670021 created "2023-07-09" @default.
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- W4383670021 date "2023-01-01" @default.
- W4383670021 modified "2023-10-18" @default.
- W4383670021 title "Detecting and Analyzing Malware Using Machine Learning Classifiers" @default.
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- W4383670021 doi "https://doi.org/10.1007/978-981-99-0483-9_18" @default.
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