Network intrusion detection systems (NIDS)
are essential for cyber security since they help find and prevent malicious
activities in the network traffic. This research uses a dataset from Kaggle and
various machine learning methods to achieve a more effective NIDS. For
analyzing dataset, an initial phase of the research a Recursive Feature
Elimination (RFE) algorithm was used to identify the most significant features
for the Random Forest Classifier. Further, Optuna has been used to find the
best model and adjust its settings when utilizing three types of classifiers:
Decision Tree, K-Nearest Neighbors (KNN) and Logistic Regression. All these
algorithms are used to detect malicious activities for enhancing network
security, and improving threat detection strategy. An exhaustive comparative
evaluation demonstrates that Decision Tree algorithms outperform other algorithms
for the effective network intrusion detection in adaptive manner with much
higher accuracy.