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- W2744431946 abstract "In this epoch, current web world where peoples groups are associated through correspondence channel and the majority of their information is facilitated on the web associated assets. Thusly the security is the significant concern of this internet community to protect the resources and to ensure the assets and the information facilitated on these networks. In current trends, the greater part of the end client are depending on the end security items, for example, Intrusion detection system, firewall, Anti-viruses etc. In this paper, we propose a machine learning based architecture to distinguish existing and recently developing malware by utilizing network and transport layer traffic features. This paper influences the precision of Semi-supervised learning in identifying new malware classes. We show the adequacy of the framework utilizing genuine network traces. Amid this research, we will execute and design the proactive network security mechanism which will gather the malware traces. Assist those gathered malware traces can be utilized to fortify the signature based discovery mechanism." @default.
- W2744431946 created "2017-08-17" @default.
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- W2744431946 date "2017-08-08" @default.
- W2744431946 modified "2023-09-27" @default.
- W2744431946 title "Real-Time Framework for Malware Detection Using Machine Learning Technique" @default.
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- W2744431946 doi "https://doi.org/10.1007/978-3-319-63673-3_21" @default.
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