TY - GEN
T1 - Prediction of DDoS Flooding Attack using Machine Learning Models
AU - Patil, Pooja S.
AU - Deshpande, S. L.
AU - Hukkeri, Geeta S.
AU - Goudar, R. H.
AU - Siddarkar, Poonam
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Nowadays multifarious types of Distributed Denial of Services attacks occur owing to the rapid growth in technology and also potentially cause harm in Software Defined Network architecture. As a consequence, it is found one among the crucial and commonly occurring cyber-Attack. There are many traditional and advanced methods for detecting these attacks. This paper intends to build a Machine Learning based model for predicting the DDoS Flooding attacks. The DDoS flooding attacks to be anticipated are involved with numerous types. The ML models used to classify these attacks are namely, Logistic Regression, K-nearest neighbour, Multi-Layer Perceptron, and, Decision Tree classifiers. The implementation is been done with a jupyter notebook with required python packages installed. Among these four classifiers, KNN and Decision Tree Classifiers have shown almost similar and best accuracy of 99.98 percent in TCP and ICMP flooding attack prediction. The Decision Tree Classifier has shown the best accuracy of 77.23 percent compared to others in UDP flooding attack prediction.
AB - Nowadays multifarious types of Distributed Denial of Services attacks occur owing to the rapid growth in technology and also potentially cause harm in Software Defined Network architecture. As a consequence, it is found one among the crucial and commonly occurring cyber-Attack. There are many traditional and advanced methods for detecting these attacks. This paper intends to build a Machine Learning based model for predicting the DDoS Flooding attacks. The DDoS flooding attacks to be anticipated are involved with numerous types. The ML models used to classify these attacks are namely, Logistic Regression, K-nearest neighbour, Multi-Layer Perceptron, and, Decision Tree classifiers. The implementation is been done with a jupyter notebook with required python packages installed. Among these four classifiers, KNN and Decision Tree Classifiers have shown almost similar and best accuracy of 99.98 percent in TCP and ICMP flooding attack prediction. The Decision Tree Classifier has shown the best accuracy of 77.23 percent compared to others in UDP flooding attack prediction.
UR - https://www.scopus.com/pages/publications/85158143733
UR - https://www.scopus.com/pages/publications/85158143733#tab=citedBy
U2 - 10.1109/ICSTCEE56972.2022.10100083
DO - 10.1109/ICSTCEE56972.2022.10100083
M3 - Conference contribution
AN - SCOPUS:85158143733
T3 - Proceedings of the 3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022
BT - Proceedings of the 3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022
A2 - Divakar, B. P.
A2 - Hulipalled, Vishwanath R.
A2 - Kodabagi, Mallikarjun M.
A2 - Devanathan, M
A2 - Parthasarathy, G
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Smart Technologies in Computing, Electrical and Electronics, ICSTCEE 2022
Y2 - 16 December 2022 through 17 December 2022
ER -