Intrusion-Detection-System-based-on-Machine-Learning-algorithms

Results

Algorithm	Accuracy
K-Nearest Neighbors (KNN)	74.41 %
K-Nearest Neighbors (KNN) after SMOTE	80.49 %
Support Vector Machine (SVM)	74.27 %
Support Vector Machine (SVM) after SMOTE	77.49%

Dataset

NSL KDD

References

M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, “A Detailed Analysis of the KDD CUP 99 Data Set,” Submitted to Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), 2009.