测试四种模型在数据投毒攻击下的抗性，发现深度模型易失效。
Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks
- 对比四种模型在真实物联网数据上的抗投毒能力
- 逻辑回归和深度网络性能下降最高达40%
- 适合关注AI安全的系统设计者和合规审查人员
确保基于机器学习的物联网入侵检测系统可靠性仍是关键挑战，尤其在数据投毒攻击日益威胁模型训练完整性的情况下。本研究评估了四种常用分类器——随机森林、梯度提升机、逻辑回归和深度神经网络——在三种真实物联网数据集上对多种投毒策略的脆弱性。结果表明，集成学习模型表现相对稳定，而逻辑回归和深度神经网络在标签篡改和异常值攻击下性能下降最高达40%。此类干扰显著扭曲决策边界，降低检测准确率，削弱部署可行性。研究强调需引入对抗鲁棒训练、持续异常监测及特征级验证，并将韧性测试纳入物联网安全的监管与合规框架。整体为构建更稳健的入侵检测流程提供实证基础，指导未来自适应、抗攻击模型的研究。
原文摘要 · Abstract (English)
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.
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