Adaptive Intrusion Detection System Leveraging Dynamic Neural Models with Adversarial Learning for 5G/6G Networks

TL;DR

This paper introduces a novel IDS framework for 5G/6G networks that uses dynamic neural networks and adversarial training to improve real-time threat detection, robustness against poisoned data, and reduce retraining needs.

Contribution

It presents an innovative IDS approach combining adversarial training, incremental learning, and dynamic neural models tailored for next-generation networks, enhancing security and efficiency.

Findings

Achieved 82.33% accuracy on NSL-KDD dataset for multiclass attack detection.

Demonstrated robustness against dataset poisoning attacks.

Reduced retraining frequency through incremental learning algorithms.

Abstract

Intrusion Detection Systems (IDS) are critical components in safeguarding 5G/6G networks from both internal and external cyber threats. While traditional IDS approaches rely heavily on signature-based methods, they struggle to detect novel and evolving attacks. This paper presents an advanced IDS framework that leverages adversarial training and dynamic neural networks in 5G/6G networks to enhance network security by providing robust, real-time threat detection and response capabilities. Unlike conventional models, which require costly retraining to update knowledge, the proposed framework integrates incremental learning algorithms, reducing the need for frequent retraining. Adversarial training is used to fortify the IDS against poisoned data. By using fewer features and incorporating statistical properties, the system can efficiently detect potential threats. Extensive evaluations using the NSL- KDD dataset demonstrate that the proposed approach provides better accuracy of 82.33% for multiclass classification of various network attacks while resisting dataset poisoning. This research highlights the potential of adversarial-trained, dynamic neural networks for building resilient IDS solutions.