Application of the Learning Set for the Detection of Jamming Attacks in 5G Mobile Networks
Brou Médard Kouassi, Vincent Monsan, Abou Bakary Ballo, Kacoutchy Jean Ayikpa, Diarra Mamadou, Kablan Jérôme Adou · International Journal of Advanced Computer Science and Applications · 2023
Jamming attacks represent a significant problem in 5G mobile networks, requiring an effective detection mechanism to ensure network security. This study focused on finding effective methods for detecting these attacks using machine learning techniques. The effectiveness of Ensemble Learning and the XGBOOST-Ensemble Learning combination was evaluated by comparing their performance to other existing approaches. To carry out this study, the WSN-DS database, widely used in attack detection, was used. The results obtained show that the hybrid method, XGBOOST-Ensemble Learning, outperforms other approaches, including those described in the literature, with an accuracy ranging from 99.46% to 99.72%. This underlines the effectiveness of this method for accurately detecting jamming attacks in 5G networks. By using advanced machine learning techniques, the present study helps strengthen the security of 5G mobile networks by providing a reliable mechanism to detect and prevent jamming attacks. These encouraging results also open avenues for future research to further improve the accuracy and effectiveness of attack detection in radiocommunication in general and specifically in 5G networks, thereby ensuring better protection for next-generation wireless communications.