A Methodological and Analytical Framework for Machine Learning and Deep Learning Based Intrusion Detection Systems

An important part of a modern cybersecurity solution is an Intrusion Detection System (IDS). They find out about changes that are happening in networks. Numerical performance metrics are the main focus of most research on machine learning and deep learning methods for Intrusion Detection Systems (IDS). This means that people tend to rely too much on numerical performance instead of looking at how the model behaves, how well it works, and how easy it is to design and deploy. We offer a thorough, systematic, and analytical framework for intrusion detection that uses classical machine learning, a deep learning architecture, and the NSL-KDD dataset as a benchmark. The suggested framework encompasses systematic data preprocessing, feature selection, class imbalance mitigation, and standardized evaluation protocols. In addition, there are new deep learning architectures, such as Convolutional Neural Networks, Long Short-Term Memory networks, and a mix of CNN and LSTM formats. This work provides a detailed analytical analysis rather than only revealing single performance metrics which can be used to characterize behavior of algorithms, architectural design, and practical deployment considerations. The suggested framework is great for applied research and making real-world IDS. and it sets up a way to repeat the process for future realistic validation.