# Detecting Jamming in Smart Grid Communications via Deep Learning
## 7 Conclusions  
This work has presented a solution to detect jamming attacks in PLC networks by considering CNNs and autoencoders as a function of the type of data considered for training the neural network model, i.e., with or without the presence of the jammer. We highlighted the trade-off between CNNs and autoencoders in detecting the presence of a jammer in the network: while autoencoders are less sensitive, thus requiring closer positioning of the jammer to the receiver, they do not require signals affected by jamming for training. Finally, we highlight that our jamming detection techniques are successful with overwhelming probability, even when the jammer is far away from the receiver, thus not affecting the quality of the link. To further strengthen the model's robustness and generalizability, we  
Figure 16: RJP analysis. Performance evaluation of Scenario 1 (CNN) and Scenario 2 (autoencoder) assuming \(\mathcal{A}\)-\(\mathcal{B}\) and \(\mathcal{E}\)-\(\mathcal{B}\) distances equal to 30 m and 65 m, respectively. (**a**) Refers to the accuracy of CNNs, while (**b**) shows the performance of the autoencoder.  
Figure 15: Image size analysis: performance evaluation of Scenario 2 (autoencoder) assuming RJP equals 1 and \(\mathcal{A}\)-\(\mathcal{B}\) distance equals 30 m while varying the distance \(\mathcal{E}\)-\(\mathcal{B}\). (**a**) Takes into account the accuracy, while (**b**) the training time.  
conducted a comprehensive hyperparameter tuning process validated through ANOVA statistical tests. We also analyzed the SNR trends and BER behavior as a function of jammer distance, offering insight into the physical-layer limitations and detection boundaries. In the future, we will investigate the additional jamming types under a real-world scenario.  
Conceptualization, M.I., A.O., J.H.F., S.S., and G.O.; methodology, M.I. and G.O.; software, M.I. and G.O.; validation, A.O. and J.H.F.; formal analysis, M.I. and G.O.; investigation, M.I., S.S., and G.O.; resources, G.O.; data curation, M.I.; writing--original draft preparation, M.I.; writing--review and editing, A.O., J.H.F., S.S., and G.O.; visualization, M.I.; supervision, S.S.; project administration, G.O.; funding acquisition, G.O. All authors have read and agreed to the published version of the manuscript.  
This publication was supported by the NPRP grant NPRP12C-0814-190012-SP165 from the Qatar National Research Fund (a member of Qatar Foundation), and Iberdrola S.A. as part of its innovation department research studies. Open Access funding provided by the Qatar National Library. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of Iberdrola Group.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Authors [PERSON] and [PERSON] were employed by the company Iberdrola Innovation Middle East. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AE & Autoencoder \\ AMI & Advanced Metering Infrastructure \\ AWGN & Additive White Gaussian Noise \\ BB-PLC & Broadband PLC \\ BER & Bit Error Rate \\ BPSK & Binary Phase-Shift Keying \\ CNN & Convolutional Neural Network \\ DL & Deep Learning \\ DoS & Denial of Service \\ FN & _False Negative_ \\ FP & _False Positive_ \\ IoT & Internet of Things \\ MSE & Mean Squared Error \\ NB-PLC & Narrowband PLC \\ PDF & Probability Density Function \\ PHY & Physical \\ PLC & Power-Line Communication \\ QPSK & Quadrature Phase-Shift Keying \\ RF & Radio Frequency \\ RJP & Relative Jamming Power \\ SG & Smart Grid \\ SNR & Signal-to-Noise Ratio \\ TNR & _True Negative Rate_ \\ TPR & _True Positive Rate_ \\ UNB-PLC & Ultra-Narrowband PLC \\ \end{tabular}