Cloud networks increasingly rely on machine learning based Network Intrusion Detection Systems to defend against evolving cyber threats. However, real-world deployments are challenged by limited labeled data, non-stationary traffic, and adaptive adversaries. While semi-supervised learning can alleviate label scarcity, most existing approaches implicitly assume benign and stationary unlabeled traffic, leading to degraded performance in adversarial cloud environments. This paper proposes a robust semi-supervised temporal learning framework for cloud intrusion detection that explicitly addresses adversarial contamination and temporal drift in unlabeled network traffic. Operating on flow-level data, this framework combines supervised learning with consistency regularization, confidence-aware pseudo-labeling, and selective temporal invariance to conservatively exploit unlabeled traffic while suppressing unreliable samples. By leveraging the temporal structure of network flows, the proposed method improves robustness and generalization across heterogeneous cloud environments. Extensive evaluations on publicly available datasets (CIC-IDS2017, CSE-CIC-IDS2018, and UNSW-NB15) under limited-label conditions demonstrate that the proposed framework consistently outperforms state-of-the-art supervised and semi-supervised network intrusion detection systems in detection performance, label efficiency, and resilience to adversarial and non-stationary traffic.

翻译：云网络日益依赖基于机器学习的网络入侵检测系统来抵御不断演变的网络威胁。然而，实际部署面临标注数据有限、非平稳流量和自适应对抗的挑战。虽然半监督学习可以缓解标签稀缺问题，但大多数现有方法隐式假设未标注流量为良性且平稳，导致在对抗性云环境中性能下降。本文提出一种用于云入侵检测的鲁棒半监督时间学习框架，明确应对未标注网络流量中的对抗污染和时间漂移。该框架基于流级数据运行，结合监督学习与一致性正则化、置信度感知伪标签以及选择性时间不变性，在保守利用未标注流量的同时抑制不可靠样本。通过利用网络流量的时间结构，所提方法在不同异构云环境中提升了鲁棒性和泛化能力。在公开数据集（CIC-IDS2017、CSE-CIC-IDS2018和UNSW-NB15）上基于有限标签条件的广泛评估表明，该框架在检测性能、标签效率以及对对抗性及非平稳流量的鲁棒性方面，持续优于最先进的监督式和半监督式网络入侵检测系统。