# Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network
Data Preprocessing: this step preprocesses the raw beacon frame data to extract relevant information, such as signal strength, MAC addresses, SSID (Service Set Identifier), and other features that can be used to identify devices. This stage involves converting and standardizing the data to make it suitable for input into a CNN. 3. CNN Model Design: the design of a CNN architecture that is suitable for RF fingerprinting should take as input the preprocessed data and output a binary classification (authorized or unauthorized device). The network can have convolutional layers to extract relevant patterns from the RF data, followed by fully connected layers for classification. 4. Model Training: This usually includes splitting the dataset into training and testing subsets, training the CNN on the training data, using binary classification (authorized or unauthorized) as the target, and using appropriate loss functions and optimization techniques for training. * Model Evaluation: Evaluating the model's performance on the testing dataset is a common metric for classification problems including accuracy, precision, recall, and F1-score. In addition, this includes fine-tuning the model and hyperparameters to optimize performance. * Real-Time Monitoring: this needs to capture and process live WLAN beacon frames in real-time, and feed these frames into the trained CNN for continuous monitoring. * Alerting and Response: If the CNN detects a device as unauthorized, the model generates an alert or takes appropriate action. This could involve disconnecting the unauthorized device from the network or notifying network administrators. * Regular Updates: this stage periodically updates the CNN model as the network evolves, and new devices are added. Re-train the model with fresh data to maintain its accuracy. * Security Measures: In addition to using an RF fingerprinting CNN, employ other security measures like encryption, strong authentication, and intrusion detection systems to enhance network security. * Compliance: this is to ensure that the network monitoring system complies with relevant regulations and privacy considerations, especially if we are handling sensitive information. [PERSON] et al. [14] addressed the constellation-error parameter using the SDA feature extraction classification method. The RF source was seven TDMA satellite terminals, and the study obtained 95% identification accuracy. [PERSON] et al. [15] discussed the parameters of frequency offset, modulation phase offset, in-phase/quadrature-phase offset, and magnitude using the weighted voting-based classifier. The RF emitter was Six WARP radio cards. The results demonstrated acquired 88% identification accuracy and a 12.8% false alarm rate. The study [16] studied the IQ Imbalance parameter using SVM classification with MATLAB to simulate 5 analog modulators. The accuracy was \(\geq\)90% for SNR \(\geq\) 15 dB. [PERSON] et al. [7] discussed frequency error, SYNC correlation, IQ offset, magnitude error, and phase error as radiometric parameters, using k-NN & SVM classification techniques, with 138 802.11 NICs RF emitters, the accuracy was 99.9% for SVM and97% for k-NN. The study [17] proposed a method for device-type and physical device categorization described by \"GTID\" depending on artificial neural networks to exploit differences in hardware compositions and clock skews of the network items. However, choosing the best set of features is difficult because so many different traits are used. When there are many devices, this also results in scalability issues, which raises the computational complexity of training. Deep neural networks offer prevailing frameworks for greatly increasing the number of layers and their neurons, leveraging large datasets, and learning complex functions. Authors of both [18; 19] applied deep neural networks at the physical layer, particularly spotlighting Convolutional Neural Network (CNN) and IQ samples-based modulation recognition to categorize 11 dissimilar modulation formats.