Application of kernel spherical k-means for intrusion detection systems

Operational system can be threatened by malicious network activities from intruders or hackers. Consequently, security of a system is indeed become an important subject to tackle this matter. Intrusion Detection System (IDS) is a system which can prevent network traffic and observe suspicious activities in network systems. Therefore, IDS can solve multiple privacy concerns. This paper will propose new method called Kernel Spherical K-Means (KSPKM) that has been modified from Spherical K-Means (SPKM) algorithm by using RBF and polynomial kernel. For our empirical study, we will be using the dataset from KDD Cup 1999 then classified types of attacks into five classes. In the end, we will see which one will produce better results in terms of classification accuracy. We found out that KSPKM succeed to improve clustering accuracy with the highest rate being 98,31% compared to SPKM.

• Publication year

  2019

• Venue

  Journal of Physics: Conference Series

• Publication date

  2019-05-01

• Fields of study

  Physics, Computer Science

• Identifiers
  DOI 10.1088/1742-6596/1218/1/012037
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Comparison between Support Vector Machine and Fuzzy C-Means as Classifier for Intrusion Detection System

  2018cited by this paper
• Intrusion Detection System using AI and Machine Learning Algorithm

  2018cited by this paper
• Intrusion detection model using fusion of chi-square feature selection and multi class SVM

  2017cited by this paper
• Fuzzy kernel C-means algorithm for intrusion detection systems

  2015cited by this paper
• Denial-of-Service, Probing & Remote to User (R2L) Attack Detection using Genetic Algorithm

  2012cited by this paper
• Detection of Attacks in an Intrusion Detection System

  2011cited by this paper
• Efficient online spherical k-means clustering

  2005influential reference

• K-Means Optimization with Kernel Gaussian Radial Basis Function in C#

  2025cites this paper
• DISCERN: diversity-based selection of centroids for k-estimation and rapid non-stochastic clustering

  2019cites this paper