The project involves a comparison of various machine learning models and a deep Q-learning model on the KDD dataset in order to build a network intrusion detection system. The project involves training and testing various models, such as the Support Vector Machine (SVM), Random Forest, Naive Bayes, Decision Tree, and Deep Q-learning model, to see how effective they are at detecting network intrusions. In research, the KDD dataset is widely used to evaluate the performance of intrusion detection systems. The project's findings can help to inform the development of a robust network intrusion detection system by providing insights into the strengths and limitations of various machine learning models.
