A Review of Machine Learning and Transfer Learning Strategies for Intrusion Detection Systems in 5G and Beyond

Mathematics, Journal Year: 2025, Volume and Issue: 13(7), P. 1088 - 1088

Published: March 26, 2025

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

Language: Английский

---

Implications of Data Leakage in Machine Learning Preprocessing: A Multi-Domain Investigation

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: July 12, 2024

Data leakage during machine learning (ML) preprocessing is a critical issue where unintended external information skews the training process, resulting in artificially high-performance metrics and undermining model reliability. This study addresses insufficient exploration of data across diverse ML domains, highlighting necessity comprehensive investigations to ensure robust dependable models real-world applications. Significant discrepancies performance due were observed, with notable variations F1 scores ROC AUC values for Breast Cancer dataset. The Tic-Tac-Toe Endgame dataset analysis revealed varying impact on like Ridge, SGD, GaussianNB, MLP, underscoring profound effect leakage. German Credit Scoring showed slight enhancements recall DT GB without leakage, indicating reduced overfitting. Additionally, such as PassiveAggressive, Nearest Centroid exhibited shifts metrics, intricate response also raw rates, 6.79% Spambase 1.99% Cancer. These findings emphasize meticulous management validation mitigate effects, which crucial developing reliable models.

Language: Английский

---

Survey on 5G Physical Layer Security Threats and Countermeasures

Sensors, Journal Year: 2024, Volume and Issue: 24(17), P. 5523 - 5523

Published: Aug. 26, 2024

With the expansion of wireless mobile networks into both daily lives individuals as well widely developing market connected devices, communication is an increasingly attractive target for attackers. As complexity cellular systems grows and respective countermeasures are implemented to secure data transmissions, attacks have become sophisticated on one hand, but at same time system can open up expanded opportunities security privacy breaches. After in-depth summary possible entry points networks, this paper first briefly reviews basic principles physical layer implementation 4G/5G systems, then gives overview from a perspective. It also provides software frameworks hardware tool-software defined radios currently in use experimenting with it discusses their capabilities. In final part, summarizes most promising families techniques detect illegitimate base stations-the machine-learning-based, localization-based, behavior-based methods.

Language: Английский

---

Intelligent parameter-based in-network IDS for IoT using UNSW-NB15 and BoT-IoT datasets

Journal of the Franklin Institute, Journal Year: 2024, Volume and Issue: unknown, P. 107440 - 107440

Published: Dec. 1, 2024

Language: Английский

---

Towards robust and efficient intrusion detection in IoMT: a deep learning approach addressing data leakage and enhancing model generalizability

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: July 30, 2024

Language: Английский

---

Organizational Readiness for Artificial Intelligence (AI) in Network Security

Advances in human resources management and organizational development book series, Journal Year: 2024, Volume and Issue: unknown, P. 205 - 246

Published: Nov. 15, 2024

This chapter explores the essential organizational and cultural prerequisites for successfully integrating Artificial Intelligence (AI) into network security. research employs a qualitative methodology, including comprehensive literature review, to analyze internal needs address ethical considerations such as bias, privacy, fairness. study examines impact of culture on acceptance effectiveness AI-based solutions. It emphasizes significance end-user trust in AI-driven security alerts. The findings highlight necessity readiness adaptation effective implementation AI security, concluding that approach is maximizing AI's potential enhancing measures. will benefit cybersecurity professionals, leaders, policymakers seeking understand navigate complexities integration

Language: Английский

---

AI-Powered Intrusion Detection and Prevention Systems in 5G Networks

2022 7th International Conference on Communication and Electronics Systems (ICCES), Journal Year: 2024, Volume and Issue: unknown, P. 834 - 841

Published: Dec. 16, 2024

Language: Английский

---

Non-Payload Based 5G Attack Detection with Qualitative Hybrid Feature Engineering

Published: Dec. 12, 2024

Language: Английский

---