# Threat Detection Model for WLAN of Simulated Data Using Deep Convolutional Neural Network
## 2 Materials and Methods
### The Developed Neural Network Architecture
Scalability: If an additional transceiver is presented in the network, then it is required to re-train the whole CNN, which is a computation- and time-heavy operation. ## 5 Conclusions and Future Investigation

This work presented a CNN-based fingerprinting RF model with computer-generated data and beacon frames to detect WLAN router impersonators. The developed deep CNN enhanced by ADAM optimizer is used to detect router impersonations of a WLAN via comparing the received signals, which includes a pair of MAC address/RF signature data for known and unknown devices. The results throughout the confusion matrix for the test data of the synthetic dataset demonstrated that the developed network architecture accurately classified the four generated MAC addresses of the known routers and the MACaddress for the remaining 10 unknown routers with 100% accuracy. This demonstrates that even in the presence of random noise, the distinctive patterns produced by the impairments can still be recognized. The experiment parameters included 4 known Routers, 10 Unknown Routers, 20 dB of SNR, 153 WLAN channel number, 5 Ghz channel Band, and 160 frame L-LTF Length in samples. All these values affect the obtained results. The challenging issue was with introducing impairments, which worsens the quality of service and raises the bit error rate (BER). For radios operating at varied SNR levels, the deterioration of impairments also varies [40]. The less degradation we must apply to radios to achieve the required BER, the lower the SNR. Assuming the SNR readings at the receiver side are quasi-static for duration T, enabling an average of SNR levels within each such time frame; we explore how to tackle this problem in this section. Such difficulties might arise when dealing with real-world noisy or dynamic environments. This study demonstrated how setting parameters like the number of known routers, unknown routers, and SNR can increase classification accuracy for comparable devices, further enhancing to account for new attack strategies and vulnerabilities that might emerge in the future. Future areas of improvements can be performed by modifying the network architecture by changing: (1) the number of convolutional layers, (2) the number of fully connected layers, and (3) the Convolutional layer parameters (padding, number of filters, and filter size). The modification also can be done by testing the model under different RF impairments and channels by adjusting the following: (1) RF impairments (dc Offset Range, freq Offset Range, and phase Noise Range variables), (2) Channel noise level (SNR input of \"_awgn_\" function), and (3) Multipath profile of Rayleigh channel (Average Path Gains and Path Delays features object). Conceptualization, O.I.D.B., S.M.J. and Y.M.A.K. ; methodology, Y.M.A.K. ; software, H.K.H. and S.M.J. ; validation, A.H.S. and H.K.H. ; formal analysis, O.I.D.B. ; investigation, Y.M.A.K. ; resources, O.I.D.B. ; data curation, H.K.H. and S.M.J. ; writing--original draft preparation, S.M.J. and Y.M.A.K. ; writing--review and editing, H.K.H. ; visualization, O.I.D.B. ; supervision, A.H.S. ; project administration, A.H.S. ; funding acquisition, A.H.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. We acknowledge all support given from the Department of Medical Technical Institute, Northern Technical University, Mosul, Iraq. The authors declare no conflict of interest. ## References

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