Open AccessNetwork Intrusion Detection System using XG Boost
Author(s) -
M. Geetha Priya,
Bipin Kumar Sahu,
B. T. Sampath Kumar,
Mayank Yadav
Publication year - 2019
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.a1307.109119
Subject(s) - intrusion detection system , computer science , the internet , intrusion , data mining , anomaly based intrusion detection system , intrusion prevention system , computer security , machine learning , world wide web , geochemistry , geology
Internet is the most widely used commodity throughout the world. Such widescale adoption of internet has resulted in drastic developments across various facets of life. Several studies indicate a surge in cybercrimes including incidents of personal privacy thefts. Network intrusion is any illegitimate and/or unidentified activity taking place over a network. So, an effective intrusion detection system is required to be developed. Through this paper, we propose an intrusion detection system that uses XG Boost algorithm to detect intrusions. To implement this approach, KDD-99 dataset has been used for inputs. This paper demonstrates that the efficiency and accuracy of intrusion detection system deployed using XG Boost algorithm is better than contemporary algorithms.