UDP Flood DDoS Attack Detection Using Multilayer Perceptron Different Optimization Techniques with Pearson, Spearman and Kendall Uncorrelated Feature Subsets
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-2742-5_52
Reference10 articles.
1. Dasari KB, Devarakonda N (2022) Detection of DDoS attacks using machine learning classification algorithms. International Journal of Computer Network and Information Security (IJCNIS) 14(6):89–97. https://doi.org/10.5815/ijcnis.2022.06.07
2. Dasari KB, Devarakonda N (2021) Detection of different DDoS attacks using machine learning classification algorithms. Ingénierie des Systèmes d’Information 26(5):461–468. https://doi.org/10.18280/isi.260505
3. Dasari KB, Devarakonda N (2022) Detection of TCP-based DDoS attacks with SVM classification with different kernel functions using common uncorrelated feature subsets. International Journal of Safety and Security Engineering 12(2):239–249. https://doi.org/10.18280/ijsse.120213
4. Al-Naymat G, Al-Kasassbeh M, Al-Harwari E (2018) Using machine learning methods for detecting network anomalies within SNMP-MIB dataset. International Journal of Wireless and Mobile Computing 15(1):67–76
5. Dasari KB, Devarakonda N (2022) SynFlood DDoS attack detection with SVM Kernels using uncorrelated feature subsets selected by Pearson, Spearman and Kendall Correlation Methods. In: 2022 second international conference on computer science, engineering and applications (ICCSEA), pp 1–6. https://doi.org/10.1109/ICCSEA54677.2022.9936114
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