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- W4386213635 abstract "While malware has posed a danger to businesses for years, advances in malware detection have lagged. Malware can cause damage to a system by starting unneeded services, which increases the system's workload and prevents it from operating smoothly. Malware detection can be done in one of two ways: the traditional signature-based approach or the more modern behavior-based approach. When malware is activated on a system, it conducts specific actions, such as launching malicious OS services or downloading malicious files from the web, that characterize its behaviour. The described technique detects malicious software based on its actions. The suggested model in this paper combines Support Vector Machine and Principal Component Analysis." @default.
- W4386213635 created "2023-08-29" @default.
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- W4386213635 date "2023-08-03" @default.
- W4386213635 modified "2023-10-17" @default.
- W4386213635 title "A Machine Learning Method for Detecting Behavior-Based Malware" @default.
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- W4386213635 doi "https://doi.org/10.1109/icirca57980.2023.10220603" @default.
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