Ensuring the reliability of machine learning-based intrusion detection systems remains a critical challenge in Internet of Things (IoT) environments, particularly as data poisoning attacks increasingly threaten the integrity of model training pipelines. This study evaluates the susceptibility of four widely used classifiers, Random Forest, Gradient Boosting Machine, Logistic Regression, and Deep Neural Network models, against multiple poisoning strategies using three real-world IoT datasets. Results show that while ensemble-based models exhibit comparatively stable performance, Logistic Regression and Deep Neural Networks suffer degradation of up to 40% under label manipulation and outlier-based attacks. Such disruptions significantly distort decision boundaries, reduce detection fidelity, and undermine deployment readiness. The findings highlight the need for adversarially robust training, continuous anomaly monitoring, and feature-level validation within operational Network Intrusion Detection Systems. The study also emphasizes the importance of integrating resilience testing into regulatory and compliance frameworks for AI-driven IoT security. Overall, this work provides an empirical foundation for developing more resilient intrusion detection pipelines and informs future research on adaptive, attack-aware models capable of maintaining reliability under adversarial IoT conditions.

翻译：确保基于机器学习的入侵检测系统的可靠性仍是物联网环境中的关键挑战，尤其是在数据投毒攻击日益威胁模型训练管道完整性的背景下。本研究利用三个真实物联网数据集，评估了四种广泛使用的分类器——随机森林、梯度提升机、逻辑回归和深度神经网络——对多种投毒策略的敏感性。结果表明，尽管基于集成学习的模型表现出相对稳定的性能，但逻辑回归和深度神经网络在标签操纵和基于异常值的攻击下性能退化高达40%。此类扰动显著扭曲决策边界、降低检测保真度并削弱部署就绪性。研究结果凸显了在运营网络入侵检测系统中引入对抗鲁棒训练、持续异常监测及特征级验证的必要性。本研究还强调将韧性测试纳入AI驱动的物联网安全监管与合规框架的重要性。总体而言，本工作为开发更具韧性的入侵检测管道提供了实证基础，并指导未来关于自适应、具有攻击感知能力的模型研究，以期在对抗性物联网条件下保持可靠性。