• G. Tejaswini Ratna Tulasi
• K. Sravani
• Ch. Akhila
• G. Surya Prakash
• K. Arun Babu

Distributed Denial of Service refers to a cyber-attack resulting in victims being unable to access systems and network resources, essentially disrupting internet services. Many machine learning techniques are there to detect the DDoS attack, but the attack remains major issue of the internet. The existing system have low detection accuracy and high false positive rates. In order to increase the accuracy and reduce the false positive rates, this paper presents a semi-supervised approach to detect the DDoS attack. Semi Supervised Approach takes the advantage of both supervised and unsupervised approaches by the ability to work on labelled and unlabeled datasets. Unsupervised part of our approach is K-Means clustering algorithm used to obtain classes to distinguish attacks from normal traffic. After that supervised algorithms of Support Vector Machine and Random Forest are applied for classification purpose. By using this approach, we can increase accuracy and reduce the false positive rates.

DDoS attacks, K-Means, Support Vector Machine, Random Forest.

"DDoS Attack Detection Based on Semi Supervised Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.f295-f298, February-2023, Available :http://www.jetir.org/papers/JETIR2302532.pdf

"DDoS Attack Detection Based on Semi Supervised Machine Learning Approach", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppf295-f298, February-2023, Available at : http://www.jetir.org/papers/JETIR2302532.pdf