A Review of Machine Learning and Transfer Learning Strategies for Intrusion Detection Systems in 5G and Beyond - Kinzah Noor; Agbotiname Lucky Imoize; Chun-Ta Li; Chi-Yao Weng (Mathematics)
This review systematically explores the application of machine learning (ML) models in the context of Intrusion Detection Systems (IDSs) for modern network security, particularly within 5G environments. The evaluation is based on the 5G-NIDD dataset, a richly labeled resource encompassing a broad range of network behaviors, from benign user traffic to various attack scenarios. This review examines multiple machine learning (ML) models, assessing their performance across critical metrics, including accuracy, precision, recall, F1-score, Receiver Operating Characteristic (ROC), Area Under the Curve (AUC), and execution time. Key findings indicate that the K-Nearest Neighbors (KNN) model excels in accuracy and ROC AUC, while the Voting Classifier achieves superior precision and F1-score. Other models, including decision tree (DT), Bagging, and Extra Trees, demonstrate strong recall, while AdaBoost shows underperformance across all metrics. Naive Bayes (NB) stands out for its computational efficiency despite moderate performance in other areas. As 5G technologies evolve, introducing more complex architectures, such as network slicing, increases the vulnerability to cyber threats, particularly Distributed Denial-of-Service (DDoS) attacks. This review also investigates the potential of deep learning (DL) and Deep Transfer Learning (DTL) models in enhancing the detection of such attacks. Advanced DL architectures, such as Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Networks (CNNs), Residual Networks (ResNet), and Inception, are evaluated, with a focus on the ability of DTL to leverage knowledge transfer from source datasets to improve detection accuracy on sparse 5G-NIDD data. The findings underscore the importance of large-scale labeled datasets and adaptive security mechanisms in addressing evolving threats. This review concludes by highlighting the significant role of ML and DTL approaches in strengthening network defense and fostering proactive, robust security solutions for future networks.