Detection and Investigation of DDoS Attacks in Network Traffic using Machine Learning Algorithms
Biswajit Mondal, Chandan Koner, Monalisa Chakraborty, Subir Gupta
Abstract
The Internet of Things (IoT) represents the start of a new age in information technology (IoT). Objects (things) such as smart TVs, telephones, and smartwatches may now connect to the Internet. New services and software improve many consumers' lives. Online lessons based on COVID-9 are also included in child education devices. Multiple device integration is becoming more widespread as the Internet of Things (IoT) grows in popularity. While IoT devices offer tremendous advantages, they may also create network disruptions. This article summarises current DDoS intrusion detection research utilizing machine learning methods. This study examines the detection performance of DDoS attacks utilizing WEKA tools using the most recent NSL KDD datasets. Logistic Regression (LR), Naive Bayes (NB), SVM, K-NN, Decision Tree (DT), and Random Forest (RF) are examples of Machine Learning algorithms. Using K-Nearest Neighbors in the presented assessment (K-NN), accuracy was attained. Finally, future research questions are addressed.