融合多种模型与隐私保护，提升网络异常检测精度与数据安全。
Privacy-Preserving Hybrid Ensemble Model for Network Anomaly Detection: Balancing Security and Data Protection
- 集成KNN、SVM、XGBoost和ANN，结合隐私保护机制。
- 在小样本和不平衡数据下仍保持高检测准确率。
- 适合注重数据隐私的网络安全系统开发者参考。
由于对敏感数据保护的关注日益增加，隐私保护的网络异常检测已成为研究热点。传统异常检测模型通常侧重于准确性，而忽视了隐私这一关键方面。本文提出一种混合集成模型，融合多种机器学习算法——K-Nearest Neighbors (KNN)、Support Vector Machines (SVM)、XGBoost 和 Artificial Neural Networks (ANN)，以同时实现高检测准确率与强数据保护。该方法引入先进的预处理技术，有效提升数据质量，应对小样本和类别不平衡问题。通过将隐私保护机制嵌入模型设计，本方案在检测性能与隐私安全之间取得显著平衡，优于现有方法。
原文摘要 · Abstract (English)
Privacy-preserving network anomaly detection has become an essential area of research due to growing concerns over the protection of sensitive data. Traditional anomaly detection models often prioritize accuracy while neglecting the critical aspect of privacy. In this work, we propose a hybrid ensemble model that incorporates privacy-preserving techniques to address both detection accuracy and data protection. Our model combines the strengths of several machine learning algorithms, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), XGBoost, and Artificial Neural Networks (ANN), to create a robust system capable of identifying network anomalies while ensuring privacy. The proposed approach integrates advanced preprocessing techniques that enhance data quality and address the challenges of small sample sizes and imbalanced datasets. By embedding privacy measures into the model design, our solution offers a significant advancement over existing methods, ensuring both enhanced detection performance and strong privacy safeguards.
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