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- W3044552005 abstract "This chapter frames in the realm of Internet of Things (IoT) and provides a new deep learning solution for securing networks connecting IoT devices. In particular, it discusses a comprehensive solution to enhancing IoT defense in the form of a new protocol for IoT security. The need for IoT security solutions was revisited after the recent attacks on 120 million devices. In the current work, deep learning is studied for critical security applications by utilizing snapshots of network traffic taken from a set of nine real-world IoT devices. To that end, a set of learning tools such as Support Vector Machines (SVM), Random Forest and Deep Neural Network (DNN) are tested on a set of real-world data to detect anomalies in the IoT networks. Obtained results provided high accuracy for all tested algorithms. Notably, DNN exhibits the highest coefficient of determination among the tested models, thus, promoting the DNN as a more suitable solution in IoT security applications. Furthermore, the DNN’s learning autonomy feature results in a time efficient real-world algorithm because it skips human intervention." @default.
- W3044552005 created "2020-07-29" @default.
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- W3044552005 date "2020-01-01" @default.
- W3044552005 modified "2023-09-25" @default.
- W3044552005 title "A Good Defense Is a Strong DNN: Defending the IoT with Deep Neural Networks" @default.
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- W3044552005 doi "https://doi.org/10.1007/978-3-030-49724-8_6" @default.
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