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The most recent dataset, UNSW-NB15, is used to train machine learning classifiers. The dataset is trained using the chosen classifiers, including K-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) classifiers. The dataset is subjected to a filter-based feature selection technique to eliminate redundant and pointless characteristics. The machine learning classifiers are compared in this investigation. Comparative analysis of these machine learning classifiers is done, and the performance of classifiers is quantified in terms of Accuracy, Mean Squared Error (MSE), Precision, Recall, True Positive Rate (TPR), and False Positive Rate (FPR).
"ANALYSIS OF ML ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION DETECTION", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 3, page no.55 - 60, March-2023, Available :http://www.ijrti.org/papers/IJRTI2303010.pdf
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator