The era of big data, which is coming with a complicated and big scope of data, has caused the increase of the possibility of network attack. One of those possible attacks is DDoS or Distributed Denial of Service. It is a type of attack that floods the network traffics, and it usually is implemented in the upper layers of the network protocol. DDoS occurs like a highway blocked by traffic jams so that traffic flow does not arrive at the desired destination. Some research generates datasets of network attacks, especially on this DDoS. They analyze the taxonomy of attacks or determine important factors that affect the corresponding attack. The method for detecting DDoS is usually done by an Intrusion Detection System (IDS) using classification and clustering methods. Machine learning has been widely used to make IDS optimal. Despite the fact that a machine learning algorithm has good adaptability to detect the attack, it needs time for processing the dataset with high dimensional data, for example, 80 features. In this paper, we propose the feature selection using feature rank and the detection using some machine learning algorithms to balance the dimensionality of data and the accuracy. We focus on detecting the PortMap DDoS attack as the reflection-based DDoS. The proposed method reaches the most effective result in 99.937% of accuracy and consumes 0.04 seconds from the Chi-square attribute evaluation with stopping criteria of 7000 with the k-NN classification method.

Original languageEnglish
Pages (from-to)347-354
Number of pages8
JournalICIC Express Letters, Part B: Applications
Issue number4
Publication statusPublished - Apr 2022


  • Classification
  • Data protection
  • Features selection
  • Network infrastructure
  • Network security


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