Authors :
RAYMOND PASCAL MENYANI; DR. S.P RAJA
Volume/Issue :
Volume 8 - 2023, Issue 3 - March
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://bit.ly/3Ka2HLn
DOI :
https://doi.org/10.5281/zenodo.7797400
Abstract :
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.
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.
