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Exploring Emerging Trends in 5G Malicious Traffic Analysis and Incremental Learning Intrusion Detection Strategies

Zihao Wang, Kar Wai Fok, Vrizlynn L. L. Thing

TL;DR

This paper addresses the challenge of detecting malicious traffic in 5G networks, where encryption and scale challenge traditional methods. It surveys 5G security and AI-based detection, proposes dataset-quality criteria for 5G traffic, and introduces incremental-learning strategies to improve detectors' robustness. Experiments on the 5G-NIDD dataset show that incremental learning significantly enhances performance on emerging traffic while highlighting catastrophic forgetting as a trade-off, with LwF and batch strategies offering tunable balances. The work provides practical guidance for building privacy-aware, real-time IDS in 5G environments and outlines directions for future incremental-learning-enabled defenses.

Abstract

The popularity of 5G networks poses a huge challenge for malicious traffic detection technology. The reason for this is that as the use of 5G technology increases, so does the risk of malicious traffic activity on 5G networks. Malicious traffic activity in 5G networks not only has the potential to disrupt communication services, but also to compromise sensitive data. This can have serious consequences for individuals and organizations. In this paper, we first provide an in-depth study of 5G technology and 5G security. Next we analyze and discuss the latest malicious traffic detection under AI and their applicability to 5G networks, and compare the various traffic detection aspects addressed by SOTA. The SOTA in 5G traffic detection is also analyzed. Next, we propose seven criteria for traffic monitoring datasets to confirm their suitability for future traffic detection studies. Finally, we present three major issues that need to be addressed for traffic detection in 5G environment. The concept of incremental learning techniques is proposed and applied in the experiments, and the experimental results prove to be able to solve the three problems to some extent.

Exploring Emerging Trends in 5G Malicious Traffic Analysis and Incremental Learning Intrusion Detection Strategies

TL;DR

This paper addresses the challenge of detecting malicious traffic in 5G networks, where encryption and scale challenge traditional methods. It surveys 5G security and AI-based detection, proposes dataset-quality criteria for 5G traffic, and introduces incremental-learning strategies to improve detectors' robustness. Experiments on the 5G-NIDD dataset show that incremental learning significantly enhances performance on emerging traffic while highlighting catastrophic forgetting as a trade-off, with LwF and batch strategies offering tunable balances. The work provides practical guidance for building privacy-aware, real-time IDS in 5G environments and outlines directions for future incremental-learning-enabled defenses.

Abstract

The popularity of 5G networks poses a huge challenge for malicious traffic detection technology. The reason for this is that as the use of 5G technology increases, so does the risk of malicious traffic activity on 5G networks. Malicious traffic activity in 5G networks not only has the potential to disrupt communication services, but also to compromise sensitive data. This can have serious consequences for individuals and organizations. In this paper, we first provide an in-depth study of 5G technology and 5G security. Next we analyze and discuss the latest malicious traffic detection under AI and their applicability to 5G networks, and compare the various traffic detection aspects addressed by SOTA. The SOTA in 5G traffic detection is also analyzed. Next, we propose seven criteria for traffic monitoring datasets to confirm their suitability for future traffic detection studies. Finally, we present three major issues that need to be addressed for traffic detection in 5G environment. The concept of incremental learning techniques is proposed and applied in the experiments, and the experimental results prove to be able to solve the three problems to some extent.
Paper Structure (17 sections, 5 equations, 9 figures, 8 tables)

This paper contains 17 sections, 5 equations, 9 figures, 8 tables.

Table of Contents

  1. Introduction
  2. 5G Security
  3. Artificial Intelligence in legitimate and malicious traffic analysis and detection
  4. Traditional Solutions for Malicious traffic analysis and detection
  5. AI-based Malicious traffic analysis and detection
  6. Challenges of AI-based malicious traffic detection models in the 5G environment.
  7. High-performance processing of the detection models
  8. Traffic data security and privacy
  9. High robustness of detection capabilities.
  10. Evaluation of publicly available network traffic datasets
  11. Methodology and Experiment Design
  12. Incremental Learning Method
  13. Dataset Selection and Analysis
  14. Experiment Setup and Performance Evaluation
  15. Experiment 1: Performance Analysis of the different incremental learning algorithms
  16. ...and 2 more sections

Figures (9)

  • Figure 1: SVM performance comparison between traditional learning and incremental learning.
  • Figure 2: Logistic regression performance comparison between traditional learning and incremental learning.
  • Figure 3: Perceptron performance comparison between traditional learning and incremental learning.
  • Figure 4: SVM model forgetting rate & accuracy vs. batch number
  • Figure 5: SVM model forgetting rate vs. accuracy
  • ...and 4 more figures