Next Article in Journal
Rack Aware Data Placement for Network Consumption in Erasure-Coded Clustered Storage Systems
Next Article in Special Issue
A Review and Classification of Assisted Living Systems
Previous Article in Journal
Roboethics: Fundamental Concepts and Future Prospects
Open AccessArticle

Effective Intrusion Detection System Using XGBoost

School of Engineering, Macquarie University, Sydney NSW 2109, Australia
Author to whom correspondence should be addressed.
Information 2018, 9(7), 149;
Received: 21 May 2018 / Revised: 15 June 2018 / Accepted: 19 June 2018 / Published: 21 June 2018
(This article belongs to the Special Issue Ambient Intelligence Environments)
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. View Full-Text
Keywords: classifiers; eXtreme Gradient Boosting (XGBoost); intrusion detection system (IDS); network socket layer-knowledge discovery in databases (NSL-KDD) classifiers; eXtreme Gradient Boosting (XGBoost); intrusion detection system (IDS); network socket layer-knowledge discovery in databases (NSL-KDD)
Show Figures

Figure 1

MDPI and ACS Style

Dhaliwal, S.S.; Nahid, A.-A.; Abbas, R. Effective Intrusion Detection System Using XGBoost. Information 2018, 9, 149.

AMA Style

Dhaliwal SS, Nahid A-A, Abbas R. Effective Intrusion Detection System Using XGBoost. Information. 2018; 9(7):149.

Chicago/Turabian Style

Dhaliwal, Sukhpreet S.; Nahid, Abdullah-Al; Abbas, Robert. 2018. "Effective Intrusion Detection System Using XGBoost" Information 9, no. 7: 149.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop