A Comparative Study of Machine Learning Algorithms for Anomaly-Based Network Intrusion Detection System-Reference-Cited by-同舟云学术

A Comparative Study of Machine Learning Algorithms for Anomaly-Based Network Intrusion Detection System

Published:2022 Issue: Volume: Page:13-21
ISSN:2367-3370
Container-title:Topical Drifts in Intelligent Computing
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Author:

Tripathi Vaibhav,Dubey Anmol,Sathvik Kesari,Subhashini N.

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-0745-6_2

Reference19 articles.

1. Aishwarya C, Venkateswaran N, Supriya T, Sreekar M, Sreeja V (2020) Intrusion detection system using KDD cup 99 dataset. Int J Innov Technol Explor Eng (IJITEE) 9(4). ISSN: 2278-3075

2. Kelly A, Johnson MA (2021) Investigating the statistical assumptions of Naïve Bayes classifiers. In: 2021 55th annual conference on information sciences and systems (CISS), pp 1–6. https://doi.org/10.1109/CISS50987.2021.9400215

3. Bhavana D, Kishore Kumar K, Chilakala V, Chithirala HG, Meka TR (2019) A comparison of various machine learning algorithms in designing an intrusion detection system. Int J Sci Technol Res 8(12)

4. Dhaliwal SS, Nahid A-A, Abbas R (2018) Effective intrusion detection system using XGBoost. Information 9(7):149. https://doi.org/10.3390/info9070149

5. Siva Priya MS, Sahu BK, Kumar B, Yadav M (2019) Network intrusion detection system using XG boost. Int J Eng Adv Technol (IJEAT) 9(1). ISSN: 2249-8958

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