Parametric Activation Functions for Improved Identity Verification in IoT Devices: A Deep Learning Approach

One of the most important solutions in designing the architecture of a deep neural network is to use suitable activation functions in the hidden layers of the network. This function plays an important role in the back propagation algorithm and the calculation of the gradient of the cost function is based on the output of the activation function. In this paper, we will model a deep neural network to address an application problem in the Internet of Things, using experimental data recorded in a smart home, with the goal of identifying and preventing unauthorized devices from entering the Internet of Things network. The method used in this study relies on the radio frequency fingerprint of a radio device connected to the Internet of Things. The database used in this study consists of 8000 samples from 15 test series, collected using the One RF Hack radio receiver in the smart home and on 4 different connected devices. Finally ,we evaluated the performance of different activation functions in the hidden layers of the network. Ultimately, the most effective activation function was selected for the efficient and effective network. The Python code of the network architecture is located in GitHub https://github.com/SaeidSarabadan/RF_with_-ANN .