深度预测模型中的特洛伊马猎人：来自欧洲航天局竞赛的见解
Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition
AI总结：
本文探讨深度预测模型中特洛伊马攻击的安全风险，通过欧洲航天局竞赛展示如何识别隐藏的触发器，总结关键洞察和研究方向。
摘要：
预测在现代安全关键应用中起着至关重要的作用，如航天操作。然而，深度预测模型的广泛应用引入了新的安全风险，即通过在训练数据或模型权重中隐藏后门进行特洛伊马攻击。一旦植入，后门在测试时通过特定触发模式激活，导致模型产生被操控的预测。我们在\textit{Trojan Horse Hunt}数据科学竞赛中聚焦此问题，超过200支队伍面临识别隐藏在航天器遥测数据中的深度预测模型触发器的任务。我们描述了新的任务制定、基准集、评估协议和最佳解决方案。我们进一步总结了时间序列预测模型中有效识别触发器的关键洞察和研究方向。所有材料均可在官方竞赛网页https://www.kaggle.com/competitions/trojan-horse-hunt-in-space上公开获取。
英文摘要：
Forecasting plays a crucial role in modern safety-critical applications, such as space operations. However, the increasing use of deep forecasting models introduces a new security risk of trojan horse attacks, carried out by hiding a backdoor in the training data or directly in the model weights. Once implanted, the backdoor is activated by a specific trigger pattern at test time, causing the model to produce manipulated predictions. We focus on this issue in our \textit{Trojan Horse Hunt} data science competition, where more than 200 teams faced the task of identifying triggers hidden in deep forecasting models for spacecraft telemetry. We describe the novel task formulation, benchmark set, evaluation protocol, and best solutions from the competition. We further summarize key insights and research directions for effective identification of triggers in time series forecasting models. All materials are publicly available on the official competition webpage https://www.kaggle.com/competitions/trojan-horse-hunt-in-space.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
