Applying Deep Learning Techniques for Network Traffic Classification: A Comparison Study on the NSL-KDD Dataset

Abstract

Abstract The escalating intricacy and refinement of network attacks require the implementation of advanced methodologies in network security and intrusion detection. This study centers on the utilization of machine learning techniques for the categorization of network traffic, specifically employing the NSL-KDD dataset. This study investigates the efficacy of various classifiers, namely Linear Support Vector Machine (SVM), Quadratic SVM, K-Nearest Neighbor (kNN), and Long Short-Term Memory (LSTM), for the precise detection of anomalous network activity. The data is preprocessed through various techniques, including one-hot encoding and normalization, in order to enhance the performance of the model. Feature selection is utilized as a means to improve the outcomes of classification. By conducting a thorough evaluation and analysis, we present an assessment of the classifiers' performance in terms of precision, recall, and F1-score. The findings demonstrate the potential application of machine learning techniques in the field of network security, underscoring the significance of carefully choosing suitable algorithms and preprocessing approaches to achieve efficient intrusion detection. The results of our study make a significant contribution to the advancement of intrusion detection systems based on machine learning. These findings offer valuable insights for both network security practitioners and researchers in the field.