A Study of Intrusion Detection System (IDS) through Machine Learning Algorithm

Registration ID: IJNRD_189356

Published ID: IJNRD2303333

DOI: http://doi.one/10.1729/Journal.33436

Preeti Gupta

ID, machine learning, NSL-KDD dataset, feature selection.

Due to the advancement of internet, the number of attacks over internet has also increased. To ensure the security of a network a good Intrusion Detection System (IDS) is required. The aim of IDS is to monitor the processes prevailing in a network and to analyse them for signs of any possible deviations. Some studies have been done in this field but a deep and exhaustive work has still not been done. This paper proposes an IDS using machine leaning for network with a good union of feature selection technique and classifier by studying the combinations of most of the popular feature selection techniques and classifiers. A set of significant features is selected from the original set of features using feature selection techniques and then the set of significant features is used to train different types of classifiers to make the IDS. Intrusion detection is performed on the dataset which contain Test data and Training data. It is finally observed that K-NN classifier produces better performance than others and, among the feature selection methods, information gain ratio based feature selection method is better.

Published Paper   E-Certificate

Preeti Gupta (March-2023). A Study of Intrusion Detection System (IDS) through Machine Learning Algorithm. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 8(3), d227-d231. http://doi.one/10.1729/Journal.33436

Volume 8 Issue 3, March-2023

Pages : d227-d231

Paper Reg. ID: IJNRD_189356

Published Paper Id: IJNRD2303333

Downloads: 000121982

Research Area: Computer Engineering 

Country: Ghaziabad, Uttar Pradesh, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2303333.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2303333

Crossref DOI: http://doi.one/10.1729/Journal.33436

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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