Collaborative Research: ELEMENTS: Tuning-free Anomaly Detection Service

合作研究：Elements：免调优异常检测服务

• 批准号: 2103832
• 负责人: Elke Rundensteiner
• 金额: $ 25.97万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2103832&HistoricalAwards=false
• 关键词: Collaborative; Research; ELEMENTS; Tuning; free

Finding and understanding anomalous behavior in data is important in many applications. A large number of anomaly detection algorithms exist, and it can be difficult to determine which algorithm is best suited to a particular domain. And once an algorithm is selected, users must tune many parameters manually to get the algorithm to perform well; this requires in-depth knowledge of the machine learning process and an understanding of the trade-offs among different algorithms to select the best performing approach. To address these difficulties, this team develops a package that can test a range of unsupervised anomaly detection techniques on a dataset, explore options to identify best-fit, and classify anomalies with higher accuracy than manual tuning.The project will automatically test a range of unsupervised anomaly techniques on a data set, extract knowledge from the combined detection results to reliably distinguish between anomalies and normal data, and use this knowledge as labels to train an anomaly classifier; the goal is to classify anomalies with an accuracy higher than what is achievable by thorough manual tuning. The approach can be applied across of a range of data types and domains. The resulting cyberinfrastructure provides tuning-free anomaly detection capabilities while making it easy to incorporate domain-specific requirements. It enables scientists and engineers having little experience with anomaly detection techniques to steer the anomaly detection process with domain expertise. Evaluation of the unsupervised anomaly detection package will use data sets and partnerships with collaborators from the Massachusetts General Hospital/Harvard Medical School, Cyber Security research, and Signify (formerly Philips Lighting) to ensure that the utility and usability of the package is verified throughout the development process. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the NSF Division of Information and Intelligent Systems within the Directorate for Computer and Information Science and Engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在许多应用中，查找和理解数据中的异常行为非常重要。存在大量异常检测算法，并且很难确定哪种算法最适合特定领域。 而且一旦选择了一种算法，用户必须手动调整许多参数才能使算法发挥良好的性能；这需要深入了解机器学习过程，并了解不同算法之间的权衡，以选择性能最佳的方法。 为了解决这些困难，该团队开发了一个包，可以在数据集上测试一系列无监督异常检测技术，探索识别最佳拟合的选项，并以比手动调整更高的准确度对异常进行分类。该项目将自动测试一系列对数据集进行无监督异常技术，从组合检测结果中提取知识，以可靠地区分异常数据和正常数据，并使用这些知识作为标签来训练异常分类器；目标是对异常进行分类，其准确度高于彻底手动调整所能达到的准确度。该方法可以应用于多种数据类型和领域。由此产生的网络基础设施提供了免调整的异常检测功能，同时可以轻松地合并特定于域的要求。它使对异常检测技术缺乏经验的科学家和工程师能够利用领域专业知识来指导异常检测过程。 对无监督异常检测包的评估将使用数据集以及与马萨诸塞州总医院/哈佛医学院、网络安全研究机构和 Signify（前身为飞利浦照明）的合作者的合作伙伴关系，以确保该包的实用性和可用性在整个过程中得到验证。发展过程。该奖项由高级网络基础设施办公室颁发，并得到计算机与信息科学与工程理事会下属的 NSF 信息与智能系统部的共同支持。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力评估进行评估，认为值得支持。优点和更广泛的影响审查标准。

项目成果

ELITE: Robust Deep Anomaly Detection with Meta Gradient

ELITE：使用元梯度进行稳健的深度异常检测

• DOI: 10.1145/3447548.3467320
• 发表时间: 2021-08
• 期刊: KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Zhang, Huayi;Cao, Lei;VanNostrand, Peter;Madden, Samuel;Rundensteiner, Elke A.
• 通讯作者: Rundensteiner, Elke A.

LANCET: labeling complex data at scale

LANCET：大规模标记复杂数据

• DOI: 10.14778/3476249.3476269
• 发表时间: 2021-07
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Zhang, Huayi;Cao, Lei;Madden, Samuel;Rundensteiner, Elke
• 通讯作者: Rundensteiner, Elke

AutoOD: Automatic Outlier Detection

AutoOD：自动异常值检测

• DOI: 10.1145/3588700
• 发表时间: 2023-05
• 期刊: Proceedings of the ACM on Management of Data
• 作者: Cao, Lei;Yan, Yizhou;Wang, Yu;Madden, Samuel;Rundensteiner, Elke A.
• 通讯作者: Rundensteiner, Elke A.

A demonstration of AutoOD: a self-tuning anomaly detection system

AutoOD 演示：自调整异常检测系统

• DOI: 10.14778/3554821.3554880
• 发表时间: 2022-08
• 期刊: Proceedings of the VLDB Endowment
• 影响因子: 2.5
• 作者: Hofmann, Dennis;VanNostrand, Peter;Zhang, Huayi;Yan, Yizhou;Cao, Lei;Madden, Samuel;Rundensteiner, Elke
• 通讯作者: Rundensteiner, Elke