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

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.

💡 Research Summary

This paper proposes a novel and adaptive Intrusion Detection System (IDS) framework designed to address the complex and evolving security challenges within 5G and 6G networks. The core innovation lies in the synergistic integration of adversarial learning, dynamic neural networks, and incremental learning algorithms into a cohesive system.

The framework begins by tackling foundational data issues using a Conditional Tabular Generative Adversarial Network (CTGAN). Recognizing the tabular nature of network traffic data and the severe class imbalance in attack types (e.g., R2L, U2R attacks are rare), CTGAN is employed to generate high-quality synthetic data. This synthetic data preserves the statistical properties of the original dataset, effectively augmenting minority attack classes and mitigating data scarcity and imbalance, which are common bottlenecks in training effective machine learning-based IDS.

To handle the unpredictable and dynamic nature of network traffic and novel attacks, the proposed IDS utilizes a Dynamic Neural Network as its core classifier. Unlike static architectures, dynamic neural networks offer inherent flexibility to manage unmodeled dynamics in the input data, making them more suitable for capturing anomalous and evolving attack patterns. Furthermore, the framework incorporates Incremental Learning algorithms. This critical feature allows the IDS model to continuously learn and adapt from new incoming data streams without the need for costly and time-consuming complete retraining. It enables the system to handle concept drift and emerging zero-day attacks efficiently, a necessity for the real-time operational demands of 5G/6G environments.

A key strength of the proposed system is its proactive defense mechanism through adversarial training. The framework simulates real-world threat scenarios, specifically data poisoning attacks, where a portion of the training data labels are maliciously flipped. By training the model on both clean and adversarially poisoned data, the IDS is fortified against such manipulation attempts, enhancing its robustness and reducing potential false positives induced by poisoned inputs.

The proposed model is extensively evaluated using the benchmark NSL-KDD dataset. The experimental results demonstrate that the integrated framework achieves a multiclass classification accuracy of 82.33% for distinguishing between normal traffic and four major attack categories (DoS, Probe, R2L, U2R). Importantly, this performance is maintained even under conditions of simulated dataset poisoning, validating the system’s resilience.

In summary, this research presents a holistic IDS solution that moves beyond singular algorithmic improvements. By combining intelligent data augmentation via CTGAN, an adaptive dynamic neural network classifier, lifelong learning capability through incremental algorithms, and robustness hardening via adversarial training, it outlines a comprehensive blueprint for building resilient, adaptive, and real-time intrusion detection systems capable of securing the next-generation 5G/6G network infrastructure.