With the rapid growth of the internet and digital services, cybersecurity threats have become increasingly sophisticated, posing significant risks to individuals, organizations, and governments. Traditional rule-based Intrusion Detection Systems (IDS) are limited in detecting novel and complex attacks, necessitating the use of intelligent solutions. Machine Learning (ML) offers promising approaches for analyzing large-scale network traffic and identifying malicious behavior patterns. This research focuses on the development of a Machine Learning-based Intrusion Detection System using benchmark datasets such as NSL-KDD and CICIDS2017. Various supervised and ensemble learning algorithms, including Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, and K-Nearest Neighbors, are implemented and compared. The models are evaluated based on accuracy, precision, recall, F1-score, and ROC-AUC metrics. Furthermore, cross-validation is employed to ensure robustness of the results. The outcome of this study highlights the potential of ML-driven IDS in improving detection rates and reducing false positives, thereby contributing to enhanced cybersecurity in real-world environments.
Intrusion Detection System, Machine Learning, Cybersecurity, Network Traffic Analysis, NSL-KDD, CICIDS2017, Random Forest, Support Vector Machine, Classification, ROC-AUC