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- W4320737108 abstract "The Internet of things is playing a vital role in human life as well as society. Many mandatory services are provided by IoT devices like GPS, cab services, healthcare, weather forecast and the list is too long. Our life depends a lot on IoT devices. But these devices use sensor technology and data streams for acquiring the different types of information. With the growth of IoT devices based on IoT DDoS attacks is increased. Machine Learning techniques are already in use for detecting malicious network traffic. In this paper, we present the Convolutional neural network technique to detect botnet traffic in IoT devices and networks. This model shows enhanced accuracy results thus causing low loss. CNN is a capable automatic hierarchy of learning the most relevant features. This paper achieved a higher accuracy rate of 99.98% result applying Convolutional neural network (CNN). It presents the feasibility of DDoS attack detection in IoT networks." @default.
- W4320737108 created "2023-02-15" @default.
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- W4320737108 date "2023-01-01" @default.
- W4320737108 modified "2023-09-29" @default.
- W4320737108 title "Intelligent Detection of DDoS Attack in IoT Network" @default.
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- W4320737108 doi "https://doi.org/10.1007/978-981-19-7982-8_15" @default.
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