# Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network  
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1 Medical Technical Institute, Northern Technical University, Mosul 41002, Iraq; [EMAIL_ADDRESS]  
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2 Iraqi Commission for Computers and Informatics, Baghdad 10009, Iraq; [EMAIL_ADDRESS]  
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3 Department of Sustainability Management, Dubai Academic Health Corporation, Oud Mehta, Dubai 4545, United Arab Emirates; [EMAIL_ADDRESS]  
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4 Department of Telecommunications and Information, High Institute of Telecommunications and Post, Baghdad 10011, Iraq; [EMAIL_ADDRESS]  
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5 Department of Computer Engineering, University of Al-Nahrain,  
are critical. Device silence technology has recently appeared, which is a technology that contributes to building device-specific signatures by collecting its information and using it in the process of identifying specific devices. This technology was used to reduce internal attacks or to reduce the wireless network's vulnerability to node falsification [6; 8]. The fundamental idea is to actively or passively extract distinctive patterns (also known as features) expressed by target devices during wireless communication. An effective device fingerprint must satisfy two characteristics: (1) the features must be stable, especially in the presence of node movement and changes in the environment, and (2) They must be impossible or very difficult to counterfeit. The first requirement renders identifiers such as mobile identification number (MIN), international mobile station equipment identity (IMEI) number, electronic serial number (ESN), or IP address. Candidates are unsuitable because all of these IDs are easily changeable using software [9; 10]. On the contrary, position-based features such as common radio signal strength (RSS) cannot be employed on their own as fingerprints because they are vulnerable to environmental and movement alters. Although most of the interests have focused on enhancing the wireless security and fingerprinting capabilities of wireless devices, it is surprising that there is no general insight in all of the current literature as the focus is sparse on the most recent fundamentals and key technologies involved. Wireless network suffers from different conventional attacks like Denial of Service (DoS), DNS poisoning, Routing Information Protocol attack, Flooding, Address Resolution Protocol spoofing, IP spoofing, etc. A firewall and simple digital identifiers are a great defense against outside attacks, but it is ineffective against insider threats. Further, RF fingerprinting involves extracting unique features (or patterns) across the protocol stack that can be used as device signatures. Indeed, the upper layers, medium access control (MAC) layer and physical layer have been utilized for radio fingerprinting [11]. However, simple unique identifiers such as MAC addresses, IP addresses, and international mobile station equipment identity (IMEI) numbers can easily be spoofed. Location-based features such as channel state information (CSI), angle of arrival (AoA), and radio signal strength (RSS) are susceptible to environmental and mobility changes. The network architecture should have effective intrusion detection and prevention systems to lessen these threats. RF signature-based identification provides a more robust solution due to (1) low computational cost, (2) high detection precision for earlier known attacks, and (3) identifying intrusion by matching the preconfigured knowledge base with captured patterns [12]. Creating a Radio Frequency (RF) fingerprinting CNN for network security using simulated Wireless Local Area Network (WLAN) frames is a complex task that involves several steps [13]. This type of system can be used to detect unauthorized or rogue devices within a network, which might include the following points:  
1. Data Collection, Preparation, and Labeling: this is done by collecting a dataset of WLAN beacon frames and labeling the data, indicating whether each frame comes from an authorized or unauthorized device. 2.