Distributed Denial of Service (DDoS) attacks are a major threat to network security, especially in the context of E-UTran New Radio Dual Connectivity (ENDC) camped cells. In this study, we propose the use of machine learning algorithms to detect DDoS attacks using Shell-Shock in ENDC camped cells. We used a supervised learning algorithm, such as Random Forest or Support Vector Machine (SVM), to identify patterns and signatures of DDoS attacks in network traffic data captured using Wireshark. The captured data was pre-processed to remove noise and irrelevant data, and the machine learning algorithm was trained on the pre-processed data. The trained algorithm was evaluated using a separate dataset that included both normal and malicious traffic. The performance of the machine learning algorithm was evaluated using several metrics, including accuracy, precision, recall, and F1-score. The results showed that the machine learning algorithm was effective in detecting DDoS attacks in ENDC camped cells. Our study highlights the potential of machine learning algorithms for enhancing network security in the face of DDoS attacks.

Keywords : Distributed Denial of Service (DDoS) attacks, E-UTran New Radio Dual Connectivity (ENDC), Shell-Shock, Machine Learning, Random Forest, Support Vector Machine (SVM), Wireshark, network security, network traffic data, accuracy, precision, recall, F1-score.