• Mr.S.R.Naresh
• N. Naveen Kumar
• R. Mukesh Kumar
• T. Praveen Raj
• P.Gnana sekar

Distributed Denial of Service attacks causes a threat to organizations, such as losses in finance and Data. Due to the unexpected nature of DDoS attacks, conventional methods of detection often prove inadequate. Machine learning (ML) algorithms have emerged as a promising solution for detecting DDoS attacks by recognizing patterns and irregularities in network traffic that may indicate an attack. This article offers a comprehensive overview of DDoS attack detection utilizing ML, covering a range of algorithms, including supervised, unsupervised, and deep learning. The article also discusses crucial features required to train ML models for detecting DDoS attacks, such as packet size, packet rate, and network flow features. Furthermore, this paper evaluates various machine learning algorithms such as random forest, cat-boost classifier, and gradient boosting by predicting their accuracy.

DDoS attack, Random Forest, Gradient Boosting, Cat-Boosting

"DDoS (Distributed Denial of Service) Attack Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 6, page no.25-33, June-2023, Available :http://www.jetir.org/papers/JETIRFZ06005.pdf

"DDoS (Distributed Denial of Service) Attack Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 6, page no. pp25-33, June-2023, Available at : http://www.jetir.org/papers/JETIRFZ06005.pdf