Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus
该研究提出了一种结合人类分析师的协作流式异常检测系统，通过集成异构检测器和基于归一化的加权共识来处理高速数据流中的异常检测问题。
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Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems
研究针对RAG系统的分布式攻击，通过在多个文档中分散虚假信息而非集中于一处，评估了不同配置下的攻击效果及防御策略。
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Streaming Hierarchical Inference with Tabular Foundation Models
为解决高吞吐量数据流中部署TFM的通信开销和延迟问题，提出HINT框架，结合边缘检索与云端TFM推理，平衡预测性能与通信成本。
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Machine Unlearning for the XGBoost Model with Network Intrusion Datasets
This work addresses the gap in machine unlearning research by introducing XGBoost-Forget, the first unlearning method tailored for XGBoost models in tabular network intrusion detection scenarios. Unlike existing approaches primarily designed for deep learning and image data, XGBoost-Forget efficiently removes specified intrusion data points without requiring full model retraining. The method incorporates a customized unlearning mechanism specifically designed for tabular network traffic data and is validated on real-world datasets such as IoT-23 and GeNIS. Experimental results demonstrate that XGBoost-Forget achieves substantial gains in unlearning efficiency while preserving predictive performance nearly equivalent to that of the original model, thereby offering a practical unlearning solution for security applications driven by tabular data.
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Collaborative Streaming Anomaly Detection with Interactive Explanations and Ensemble Consensus
该研究提出了一种结合人类分析师的协作流式异常检测系统，通过集成异构检测器和基于归一化的加权共识来处理高速数据流中的异常检测问题。
0 citationsRead paper
CityLearn v3: A Configurable Simulation and Evaluation Framework for Realistic Control Studies of Renewable Energy Communities
本文介绍了CityLearn v3，一个可配置的仿真和评估框架，用于在真实条件下研究可再生能源社区（REC）的控制问题，包括成员、资产变化及数据质量等。
0 citationsRead paper
Micro-Collaborative Poisoning: A Distributed Attack on RAG Systems
研究针对RAG系统的分布式攻击，通过在多个文档中分散虚假信息而非集中于一处，评估了不同配置下的攻击效果及防御策略。
0 citationsRead paper
Streaming Hierarchical Inference with Tabular Foundation Models
为解决高吞吐量数据流中部署TFM的通信开销和延迟问题，提出HINT框架，结合边缘检索与云端TFM推理，平衡预测性能与通信成本。
0 citationsRead paper
Machine Unlearning for the XGBoost Model with Network Intrusion Datasets
This work addresses the gap in machine unlearning research by introducing XGBoost-Forget, the first unlearning method tailored for XGBoost models in tabular network intrusion detection scenarios. Unlike existing approaches primarily designed for deep learning and image data, XGBoost-Forget efficiently removes specified intrusion data points without requiring full model retraining. The method incorporates a customized unlearning mechanism specifically designed for tabular network traffic data and is validated on real-world datasets such as IoT-23 and GeNIS. Experimental results demonstrate that XGBoost-Forget achieves substantial gains in unlearning efficiency while preserving predictive performance nearly equivalent to that of the original model, thereby offering a practical unlearning solution for security applications driven by tabular data.
0 citationsRead paper