• Pravin Tukaram Zhunjare
• Prof. Lalita B. Randive

In this research work discuss on the nature of the threats posed by Distributed Denial of Service (DDoS) attacks on large networks, such as the Internet, demands effective detection and response methods. These methods must be deployed not only at the edge but also at the core of the network. This paper presents methods to identify DDoS attacks by computing entropy and frequency-sorted distributions of selected packet attributes. The DDoS attacks show anomalies in the characteristics of the selected packet attributes. The detection accuracy and performance are analyzed using live traffic traces from a variety of network environments ranging from points in the core of the Internet to those inside an edge network. The results indicate that these methods can be effective against current attacks and suggest directions for improving detection of more stealthy attacks. We also describe our detection-response prototype and how the detectors can be extended to make effective response decisions.

Distributed denial of service (DDoS), Attack Detection, Machine Learning, Neural Network, ANN Approach, MATLAB, 5G Network

"DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION USING MACHINE LEARNING FOR INTERNET OF THINGS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.a22-a30, February-2024, Available :http://www.jetir.org/papers/JETIR2402003.pdf

"DISTRIBUTED DENIAL OF SERVICE ATTACK DETECTION USING MACHINE LEARNING FOR INTERNET OF THINGS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppa22-a30, February-2024, Available at : http://www.jetir.org/papers/JETIR2402003.pdf