Experimental Analysis of k-Nearest Neighbor, Decision Tree, Naive Baye, Support Vector Machine, Logistic Regression and Random Forest Classifiers with Combined Classifier Approach for NIDS
DOI:
https://doi.org/10.26438/ijcse/v6i9.940943Keywords:
Network intrusion,, support vector machine, decision tree, Decision Tree, detectionAbstract
In traditional studies about the classification, there are three non-parametric classifiers, Random Forest (RF), kNearest Neighbor (kNN), and Support Vector Machine (SVM), has been said as the most classifiers at producing excessive accuracies. In this study, Tested and Compared the performances of the kNN, Naïve Baye, Decision Tree, Support Vector Machine, Random Forest, Logistic Regression and Combined model over DOS and Normal attacks. These algorithms are among the most influential data mining algorithms in the research community. The detection of fraudulent attacks is considered as a classification problem. In this experiments have performed on different classification methods with the hybrid model on KDDCup99 Dataset. Here compared classifiers using models accuracy with confusion matrix. Cross-Validation means score used for efficiency. For this experiments used python and R programming for implementation. The different types of attacks are routine, DoS, Probe attacks, R2L, and U2R attacks.
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