用AutoML自动构建集成模型，提升网络入侵检测准确率。
An AutoML-based approach for Network Intrusion Detection
- 用MLJAR框架自动选择模型、调参和特征工程，整合LightGBM等算法。
- 在NSL-KDD数据集上达90%准确率、89%F1分数，显著低于其他单模型。
- 适合安全团队快速部署高精度检测系统，减少人工调参负担。
本文提出一种基于自动化机器学习（AutoML）的网络入侵检测方法，利用MLJAR AutoML框架构建堆叠集成模型。该方法融合LightGBM、CatBoost和XGBoost等多种机器学习算法，通过自动完成模型选择、特征工程和超参数调优，降低传统机器学习方法的手动成本。在NSL-KDD数据集上的大量实验表明，堆叠集成模型优于各单个模型，实现90%准确率和89% F1分数，显著低于随机森林（78%准确率，78% F1分数）、XGBoost和CatBoost（均为80%准确率，80% F1分数）以及LightGBM（78%准确率，78% F1分数），展现出更高的检测精度与鲁棒性，为网络安全应用提供更高效、自适应的解决方案。
原文摘要 · Abstract (English)
In this paper, we present an automated machine learning (AutoML) approach for network intrusion detection, leveraging a stacked ensemble model developed using the MLJAR AutoML framework. Our methodology combines multiple machine learning algorithms, including LightGBM, CatBoost, and XGBoost, to enhance detection accuracy and robustness. By automating model selection, feature engineering, and hyperparameter tuning, our approach reduces the manual overhead typically associated with traditional machine learning methods. Extensive experimentation on the NSL-KDD dataset demonstrates that the stacked ensemble model outperforms individual models, achieving high accuracy and minimizing false positives. Our findings underscore the benefits of using AutoML for network intrusion detection, as the AutoML-driven stacked ensemble achieved the highest performance with 90\% accuracy and an 89\% F1 score, outperforming individual models like Random Forest (78\% accuracy, 78\% F1 score), XGBoost and CatBoost (both 80\% accuracy, 80\% F1 score), and LightGBM (78\% accuracy, 78\% F1 score), providing a more adaptable and efficient solution for network security applications.
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