DDoS Attacks Classification using Numeric Attribute-based Gaussian Naive Bayes

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.

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