International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 6 Issue 6
November-December 2024
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A Survey on different Machine Learning algorithms that are compatible with CSE-CIC IDS 2018 Dataset
Author(s) | Radhika S K, Ashwini S P, Dr. Jalesh Kumar |
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Country | India |
Abstract | This paper mainly devotes on these machine learning models: Decision Trees, Naive Bayes, Gradient descent, support vector machine and Random Forest describing different potential threats represented with CSE-CIC-IDS2018 dataset. Multiclass classifications have been included to check which of the machine models will effectively identify and prevent intrusion into the network. |
Keywords | Cyber security, Machine Learning, CIC-IDS-2018, Multiclass classification, Decision Tree, Random Forest, Gradient Boost, SVM, KNN, Naïve Bayes |
Field | Computer > Network / Security |
Published In | Volume 6, Issue 6, November-December 2024 |
Published On | 2024-11-05 |
Cite This | A Survey on different Machine Learning algorithms that are compatible with CSE-CIC IDS 2018 Dataset - Radhika S K, Ashwini S P, Dr. Jalesh Kumar - IJFMR Volume 6, Issue 6, November-December 2024. |
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E-ISSN 2582-2160
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IJFMR DOI prefix is
10.36948/ijfmr
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