用动态神经网络和对抗学习提升5G/6G网络入侵检测能力
Adaptive Intrusion Detection System Leveraging Dynamic Neural Models with Adversarial Learning for 5G/6G Networks
- 采用动态神经网络与增量学习，减少频繁重训练
- 在NSL-KDD数据集上实现82.33%多类攻击分类准确率
- 对抗训练增强抗数据污染能力，适合实时安全防护场景
入侵检测系统（IDS）是保护5G/6G网络免受内外部网络威胁的关键组件。传统基于特征的检测方法难以应对新型和演进式攻击。本文提出一种先进框架，结合对抗训练与动态神经网络，在5G/6G网络中实现鲁棒、实时的威胁检测与响应。相比传统模型需高成本重训练，该框架集成增量学习算法，降低更新知识的开销。通过对抗训练强化系统对中毒数据的防御能力，仅使用少量特征并融合统计特性，提升检测效率。在NSL-KDD数据集上的广泛评估表明，该方法在多类网络攻击分类中达到82.33%的准确率，并具备抵抗数据集污染的能力。研究展示了对抗训练与动态神经网络在构建韧性入侵检测系统中的潜力。
原文摘要 · Abstract (English)
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.
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