Detection and Investigation of DDoS Attacks in Network Traffic using Machine Learning Algorithms
Biswajit Mondal1, Chandan Koner2, Monalisa Chakraborty3, Subir Gupta4
1Biswajit Mondal, Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, West Bengal 713206, India.
2Dr. Chandan Koner, Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, West Bengal 713206, India.
3Monalisa Chakraborty, Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, West Bengal 713206, India..
4Dr. Subir Gupta*, Department of Computer Science and Engineering, Dr. B. C. Roy Engineering College, Durgapur, West Bengal 713206, India.
Manuscript received on 02 April 2022. | Revised Manuscript received on 05 April 2022. | Manuscript published on 30 May 2022. | PP: 1-6 | Volume-11 Issue-6, May 2022. | Retrieval Number: 100.1/ijitee.F98620511622 | DOI: 10.35940/ijitee.F9862.0511622
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Keywords: DDoS Attacks; Internet Of Things; Machine Learning
Scope of the Article: Machine Learning