• Ravi Alawe
• Shivank soni

This paper present the threats posed by distributed denial of service (DDoS) attacks on large networks such as the Internet require the implementation of efficient detection and response strategies. These strategies should be deployed not only at the periphery of the network but also at its core. This article introduces techniques for identifying DDoS attacks by estimating the entropy and frequency-ordered distribution of specific packet characteristics. DDoS attacks reveal inconsistencies in the characteristics of these packet attributes. The detection efficacy and performance are evaluated using real-time traffic data collected from diverse network environments spanning from core Internet points to edge networks. The findings demonstrate the effectiveness of these techniques against current attacks and propose avenues to enhance detection of more covert attacks. Furthermore, we outline our detection-response prototype and discuss how these detectors can be expanded to make informed response decisions

"Capturing the Distrubuted Denial of Service Attack Using Bayesian Regularization Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.e769-e778, April-2024, Available :http://www.jetir.org/papers/JETIR2404485.pdf

"Capturing the Distrubuted Denial of Service Attack Using Bayesian Regularization Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppe769-e778, April-2024, Available at : http://www.jetir.org/papers/JETIR2404485.pdf