P-ISSN: 2707-661X, E-ISSN: 2707-6628
Printed Journal | Refereed Journal | Peer Reviewed Journal
2021, Vol. 2, Issue 1, Part A
Anomaly detection model based on SVM & XGBoost to detect network intrusions
Author(s): Manish Khule and Neha Sharma
Abstract: There are rapidly increasing attacks on computers creates a problem for network administration to prevent the computer from these attacks. There are many traditional intrusion detection systems (IDS) is present but they are unable to prevent computer system completely. The need to secure networks has increased as the number of people connecting to the network are increasing rapidly and using networks for storing or accessing critical information. In this paper we have assessed and compared various machine learning algorithm and then propose a system based on the best performing algorithm. In this we proposed an XGBoost learning technique which is an combining diverse set of learners (individual models) together to improvise on the stability and predictive power of the model.
DOI: 10.33545/2707661X.2021.v2.i1a.21
Pages: 05-09 | Views: 707 | Downloads: 252
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How to cite this article:
Manish Khule, Neha Sharma. Anomaly detection model based on SVM & XGBoost to detect network intrusions. Int J Commun Inf Technol 2021;2(1):05-09. DOI: 10.33545/2707661X.2021.v2.i1a.21
