Effective intrusion detection system using XGBoost

Sukhpreet Singh Dhaliwal, Abdullah-Al Nahid, Robert Abbas

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)
740 Downloads (Pure)


As the world is on the verge of venturing into fifth-generation communication technology and embracing concepts such as virtualization and cloudification, the most crucial aspect remains "security", as more and more data get attached to the internet. This paper reflects a model designed to measure the various parameters of data in a network such as accuracy, precision, confusion matrix, and others. XGBoost is employed on the NSL-KDD (network socket layer-knowledge discovery in databases) dataset to get the desired results. The whole motive is to learn about the integrity of data and have a higher accuracy in the prediction of data. By doing so, the amount of mischievous data floating in a network can be minimized, making the network a secure place to share information. The more secure a network is, the fewer situations where data is hacked or modified. By changing various parameters of the model, future research can be done to get the most out of the data entering and leaving a network. The most important player in the network is data, and getting to know it more closely and precisely is half the work done. Studying data in a network and analyzing the pattern and volume of data leads to the emergence of a solid Intrusion Detection System (IDS), that keeps the network healthy and a safe place to share confidential information.

Original languageEnglish
Article number149
Pages (from-to)1-24
Number of pages24
JournalInformation (Switzerland)
Issue number7
Publication statusPublished - Jul 2018

Bibliographical note

Copyright the Author(s) 2018. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.


  • classifiers
  • eXtreme Gradient Boosting (XGBoost)
  • intrusion detection system (IDS)
  • network socket layer-knowledge discovery in databases (NSL-KDD)


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