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- W3044356086 abstract "IoT is a revolutionary technology that brings together the world's living and non-living things. IoT deploymentis growing rapidly but cybersecurity remains a loophole, so that it is likely to lead to numerous cyber-attacksand it is very important for the achievement of each system that the system is totally secure something else theuser might not use the technology. DDoS assault has recently targeted a large number of IoT networks andcontributed to massive losses. In this article we have proposed a consolidated methodology for the identificationof pilfered records from programming and malware all through the IoT organize. It is proposed to characterizepilfered programming utilizing source code literary theft utilizing the TensorFlow profound neural system. Tochannel boisterous information and to additionally improve the significance of every token concerning thecounterfeiting of the source code, the tokenization and gauging techniques. This method is likewise used todistinguish literary theft in source code. Google Code Jam (GCJ) accumulates the dataset to explore the robberyof utilizations. Furthermore, the profound neural system is utilized to recognize vindictive contaminations bycolor image representation in the IoT network. The samples of malware are collected from the experimentalMaling dataset. The findings show that the classification efficiency of the approach being proposed forevaluating cyber security risks in IoT is higher than state-of-the-art methods." @default.
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- W3044356086 date "2020-01-01" @default.
- W3044356086 modified "2023-09-27" @default.
- W3044356086 title "IOT DEEP LEARNING BASED DETECTION OFCYBER SECURITY THREATS" @default.
- W3044356086 hasPublicationYear "2020" @default.
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