Leveraging machine learning for enhanced cybersecurity: an intrusion detection system

Classification remains vital in cybersecurity since different data require different protection measures against threats. This paper compares the Decision Tree, K-Nearest Neighbor (KNN) and Logistic Regression algorithms in tackling cybersecurity datasets especially ones that are unbalanced. The performance of the models was evaluated with the help of Mean Absolute Error (MAE), Mean Squared Error (MSE), RMSE, R2 Score and Accuracy along with the Classification Report which gives precision, recall, F1-score and support of each class. From the table, it is evident that the Decision Tree classifier performs the best in terms of error rates hence obtaining the accuracy of 98. Part B: Multiple regression analysis of the comparative market analysis values, for clients the R2 Score attained was 09% and an R2 Score of 89. 56%. Despite fairly good results obtained by KNN, its accuracy was not equally effective across the minority classes. Relative to the other models, Logistic Regression, was the most accurate but it recorded the highest error rate. Based on that the results, it can be concluded that even though Decision Tree models turned out to be better for this purpose, KNN and the Logistic Regression could be better if they are to be optimized.