# Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network
However, these studies only recognized the modulation type of transmitters and didn't recognize router impersonation. Several related works have been done on deep learning-based device identification to identify a device in a WLAN based on its RF fingerprinting of transmitted signals. There are a number of different security aspects and applications. We can classify these studies into supervised and unsupervised deep neural networks. The unsupervised deep learning networks depend on a real-time grouping of samples as discussed in [20], where a non-parametric Bayesian method was used for device detection, while [21] depends on an infinite hidden Markov random field (iHMRF) model. In contrast, the supervised deep learning methods are based on a priori labeling of samples and can be further classified into similarity-based and classification-based techniques. The similarity-based method uses the matching concept with database entries, where the study [22] used the 802.11 wireless driver fingerprinting model, and [23] proposed data rate fingerprinting to attain inactive localization. The supervised classification-based models relied on unique class identification and can be sub-classified into conventional (handcraft feature extraction) [24; 25]with frequency domain approach [7], with PARADIS fingerprinting, and [17] with GTID fingerprinting. Deep learning (multi-layer neural network) has been discussed by [26], which is based on modulation recognition CNN, and [27] with Deep learning of the physical layer. We are interested in researching hardware characteristics that are built into a device and that are also difficult and constant for malicious agents to duplicate. Deep CNN was applied at the physical layer in references [18; 28], with a focus on modulation recognition. The studies categorized eleven various modulation systems. However, this method merely identifies the transmitter's modulation type, not a specific device as we do here. This paper's major contribution is the development of a convolutional neural network (CNN) architecture [29; 30] for radio frequency (RF) fingerprinting utilizing computer-produced data. We employed simulated WLAN beacon frames for training the created CNN from unknown and known RF fingerprint routers. Then, we compared the RF fingerprint indicated by the CNN with the MAC (Media Access Control) address of the received signals, and we combined an advanced deep-learning convolutional neural network with the ADAM optimizer to detect the WLAN router intrusions. Adam optimizer [31; 32; 33] is considered in this work as it merges the benefits of RMSProp [34] and AdaGrad [35] optimization techniques, which actively adapts the exponential decline rate for the first and second moment measures for updating parameters. In addition, [PERSON]'s algorithm is suitable for large amounts of data sparse gradients and non-stationary objective optimization with noise. Many parameters are frequently present in deep neural networks. Since most loss functions used in deep learning are convex functions [36], finding the global optimal solution is simple with [PERSON]'s optimization of the deep learning model and leads to the best convergence.