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DOI: 10.14569/IJACSA.2017.080806
PDF

DDoS Attacks Classification using Numeric Attribute-based Gaussian Naive Bayes

Author 1: Abdul Fadlil
Author 2: Imam Riadi
Author 3: Sukma Aji

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 8, 2017.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Cyber attacks by sending large data packets that deplete computer network service resources by using multiple computers when attacking are called Distributed Denial of Service (DDoS) attacks. Total Data Packet and important information in the form of log files sent by the attacker can be observed and captured through the port mirroring of the computer network service. The classification system is required to distinguish network traffic into two conditions, first normal condition, and second attack condition. The Gaussian Naive Bayes classification is one of the methods that can be used to process numeric attribute as input and determine two decisions of access that occur on the computer network service that is “normal” access or access under “attack” by DDoS as output. This research was conducted in Ahmad Dahlan University Networking Laboratory (ADUNL) for 60 minutes with the result of classification of 8 IP Address with normal access and 6 IP Address with DDoS attack access.

Keywords: Distributed Denial of Service (DdoS); Gaussian Naive Bayes; Numeric

Abdul Fadlil, Imam Riadi and Sukma Aji, “DDoS Attacks Classification using Numeric Attribute-based Gaussian Naive Bayes” International Journal of Advanced Computer Science and Applications(IJACSA), 8(8), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080806

@article{Fadlil2017,
title = {DDoS Attacks Classification using Numeric Attribute-based Gaussian Naive Bayes},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080806},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080806},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {8},
author = {Abdul Fadlil and Imam Riadi and Sukma Aji}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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