Wireless Network Security Prediction Using Machine Learning and Graph Neural Networks

Abstract: We study the application of graph neural networks and machine learning in the prediction of risks to wireless networks. Advanced predictive methods are necessary, since mere security measures cannot thwart the rising complexity of cyber-attacks. In the present work, we will use a hybrid model that combines several machine learning methods to reduce false negatives and positives and improve accuracy in the prediction of risks to wireless networks. Models are trained and validated with large amounts of data that include the performance indicators for accuracy, precision, recall, F1-score, and AUC-ROC. From this, it is also seen that the hybrid model performs much better than the standard models in real-time threat detection. The impact of the size of the dataset on the performance of the model was also studied, and it has come out that larger datasets improve predictive powers significantly. The result demonstrated that the latest advancements in machine learning techniques can lead to important improvements in the security of wireless networks.