Electronic Theses and Dissertations
Date of Award
Master of Science
Mohd. Hasan Ali
Today, due to the increase in usages of huge communication devices and connections, network services are improving expeditiously. From 1G to 4G, the network aid unable to cover all the expectations required for a vast network. A scaling up new technology, 5G, provides more capabilities of connecting billions of devices simultaneously. In this study, to analyze the network performance of 5G, a couple of network parameters (frequency, distance) have been adjusted. Besides, Distributed Denial of Service attacks were tested on the proposed 5G network. The individual simulation outcomes were compared and generated multiple results of application throughput. From the results, increasing the DoS attacks reduces the application throughput drastically. Later the simulated data used to create a DDoS dataset for the classification purpose. Support Vector Machine (SVM), K-Nearest Neighbor(KNN), and Naive Bayes (NB) were employed for the classification where KNN performs impressively well over SVM and NB with high accuracy.
Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.
Shorna, Sabira Khanam, "Performance Analysis of 5G DDoS Attack Using Machine Learning" (2021). Electronic Theses and Dissertations. 2201.
Data is provided by the student.