Deep Learning-based Wireless Signal Classification in the IoT Environment

Abstract

With the development of the Internet of Things (IoT), diverse wireless devices are increasing rapidly. Those devices have different wireless interfaces that generate incompatible wireless signals. Each signal has its own physical characteristics with signal modulation and demodulation scheme. When there exist different wireless devices, they can suffer from severe Cross-Technology Interferences (CTI). To reduce the communication overhead due to the CTI in the real IoT environment, a central coordinator can be able to detect and identify wireless signals existing in the same communication areas. This paper investigates how to classify various radio signals using Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and attention mechanism. CNN can reduce the amount of computation by reducing weights by using convolution, and LSTM belonging to RNN models can alleviate the long-term dependence problem. Furthermore, attention mechanism can reduce the short-term memory problem of RNNs by reexamining the data output from the decoder and the entire data entered into the encoder at every point in time. To accurately classify radio signals according to their weights, we design a model based on CNN, LSTM, and attention mechanism. As a result, we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences. In a case of the real IoT environment with Wi-Fi, Bluetooth and ZigBee devices, we can normally obtain wireless signals from 10 to 20 dB. The accuracy of CLARINet's radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio (SNR) exhibits high accuracy at 16 dB to about 92.03%.

Detection of Fake Replay Attack Signals on Remote Keyless Controlled Vehicles Using Pre-Trained Deep Neural Network

“…The table compares the models in terms of learning approach, number of classes, detection accuracy, detection precision, and detection recall. Furthermore, the table considers the comparison of the proposed model with six other models, including (1) Roh et al model [41], which is implemented using a hybrid deep learning technique comprising the use of the convolutional neural network (CNN) along with the long, short-term memory (LSTM); (2) Tariq et al model [42], which is called CANTransfer and implemented using the transfer learning technique of deep cascaded model comprising several CNN-LSTM units; (3) Javed et al model [43], which is called CANintelliIDS and implemented using convolutional attention incorporated with gated recurrent neural network (GRU); (4) Song et al model [44], which is implemented using a deep convolutional neural network (DCNN); (5) Kang et al model [45], which is implemented by incorporating the deep neural networks with deep belief networks (DNN-DBN); and finally, (6) Seo et al model [46], which is called GIDS-CNN (Generative Adversarial Nets IDS -CNN). According to the table, the proposed model outperforms others in several performance indicators.…”

Detection of Fake Replay Attack Signals on Remote Keyless Controlled Vehicles Using Pre-Trained Deep Neural Network

“…The table compares the models in terms of learning approach, number of classes, detection accuracy, detection precision, and detection recall. Also, the table considers the comparison of the proposed model with six other models, including (1) Roh et al model [38], which is implemented using a hybrid deep learning technique comprising the use of the convolutional neural network (CNN) along with the long, short-term memory (LSTM), (2) Tariq et al model [39] which is called CANTransfer and implemented using the transfer learning technique of deep cascaded model comprising several CNN-LSTM units, (3) Javed et al model [40] which is called CANintelliIDS and implemented using convolutional attention incorporated with gated recurrent neural network (GRU), (4) Song et al model [41] which is implemented using a deep convolutional neural network (DCNN), (5) Kang et al model [42] which is implemented by incorporating the dee p neural networks with deep belief networks (DNN-DBN), and finally, (6) Seo et al model [43] which is called GIDS-CNN (Generative Adversarial Nets IDS -CNN). According to the table, the proposed model outperforms others in several performance indicators.…”