Intrusion Detection Systems (IDS) play a crucial role in safeguarding modern network infrastructures
by identifying malicious activities and preventing potential security breaches. This study presents a
comparative analysis of machine learning algorithms—Decision Trees, Support Vector Machines
(SVM), Random Forest, and K-Nearest Neighbors (KNN)—to evaluate their effectiveness in intrusion
detection. Using standard datasets such as KDD Cup 99 and NSL-KDD, each algorithm was tested
based on accuracy, precision, recall, and F1-score. The results show that Random Forest outperforms
other models with an accuracy of 95.3% and an F1-score of 94.2%, followed by SVM with a strong
performance in high-dimensional data classification. Decision Trees demonstrated a reasonable balance
between interpretability and performance, while KNN struggled with scalability and high-dimensional
network traffic. These findings highlight the importance of selecting the appropriate machine learning
technique for IDS, based on the specific requirements of the network environment and the complexity of
potential threats.
