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Research Article

DDoS Attack Detection Using Machine Learning

Jayashree C. Pasalakar1 Rutuja Ilag2
12 Information Technology, AISSMS IOIT, Pune, India.

Published Online: November-December 2022

Pages: 176-179

Cite this article

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References

1] Net Losses: Estimating the global cost of cybercrime. Technical report, Intel Security Group, 2014.
2] Natara ,Fu j et al. Binary Data Analysis IEE 2011
3] David et al used various methods for detecting the malicious ransom ware 2015 IEEE
4] R. Miao, R. Potharaju, M. Yu, and N. Jain, Cloud Characterizing, ACM, 2015.
5] P. Singh and M. Khari, Black hole analysis Springer, 2021.
6] F.Y. Lee and S. Shieh, "Defending against spoofed DDoS attacks with path fingerprint." International Journal of Computers and Security,
Elsevier, March 2005.
7] A. Yudhana, I. Riadi, F. J. I. J. O. A. C. S. Ridho, and APPLICATIONS, "DDoS Classification Using Neural Network and Naïve Bayes
Methods for Network Forensics," 2018.
8] H. Wang, C. Jin, and K.G. Shin, "Defense Against Spoofed IP Traffic Using Hop-Count Filtering." IEEE/ACM Transactions on
Networking, Feb. 2007.
9] M. Zakarya, “Ddos verification and attack packet dropping algorithm in cloud computing,” World A

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