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Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/3394
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dc.contributor.authorKoli, Preety-
dc.contributor.authorKanade, D. M.-
dc.contributor.authorPatil, Priyanka-
dc.contributor.authorAgrawal, Radhika-
dc.date.accessioned2022-08-06T10:14:04Z-
dc.date.available2022-08-06T10:14:04Z-
dc.date.issued2022-04-15-
dc.identifier.urihttp://192.168.3.232:8080/jspui/handle/123456789/3394-
dc.description.abstractThe major goal of proposed system is to elaborate on the severity of the Malware problem and to project the importance of online malware analysis in Malware defense research. Malware is one of the most serious Internet security threats.The proposed machine learning architectures are capable of learning features from raw data.Malware detection requires advanced techniques to reduce malware threads that can disrupt computer operation [2].It may simply find the malware included in that file using these features. It is simple to detect using a classification Machine Learning algorithm and is equivalent to other approaches [3].The use of two detection methods increases the malware's security. It not only protects it from viruses transmitted over the internet, but it also protects it from malware installed on the device. As this malware system works on machine learning, it can be easy for it to be trained to detect new malware threats.We've demonstrated that the operations and behaviours typically associated with malware cannot be considered a critical component for malware detection because benign files can conduct them as wellen_US
dc.subjectMalwareen_US
dc.subjectSecurityen_US
dc.subjectKNN Classifieren_US
dc.subjectThreatsen_US
dc.titleDetection of Malware for System Securityen_US
Appears in Collections:Computer

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