Collaborative Research: ELEMENTS: Tuning-free Anomaly Detection Service

合作研究：Elements：免调优异常检测服务

基本信息

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

项目摘要

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.

在许多应用程序中，查找和理解数据中的异常行为很重要。存在大量的异常检测算法，并且很难确定哪种算法最适合特定域。一旦选择了算法，用户必须手动调整许多参数才能使算法表现良好；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，以选择最佳性能方法。为了解决这些困难，该团队开发了一个包装，可以测试数据集上的一系列无监督的异常检测技术，探索以识别最佳拟合的选项，并分类具有更高准确性的异常，而不是手动调谐。项目将自动测试一系列不受欢迎的知识，从数据集中划分的知识，与数据集相关的范围，因为数据集相关的侦探结果范围均可依次征收范围的范围，且范围均可依次，因为数据集相关的范围内的范围内征用范围的范围，该范围是构成的范围，这些信息均可及标签培训异常分类器；目的是将准确性的异常分类高于通过彻底的手动调整所能实现的异常。该方法可以跨多种数据类型和域进行应用。由此产生的网络基础结构提供了无调的异常检测功能，同时使其易于合并特定于域的要求。它使科学家和工程师几乎没有经验的异常检测技术经验，可以用域专业知识来指导异常检测过程。评估无监督的异常检测包将使用马萨诸塞州综合医院/哈佛医学院，网络安全研究的数据集和合作伙伴关系，并在整个开发过程中验证了该软件包的实用性和可用性。高级Cyberinfrasture办公室的奖项由NSF的计算机和信息科学与工程局内的NSF信息和智能系统共同支持。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来支持的。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A Demonstration of AutoOD: A Self-tuning Anomaly Detection System

DOI：
10.14778/3554821.3554880
发表时间：
2022-08
期刊：
Proc. VLDB Endow.
影响因子：
0
作者：
Dennis M. Hofmann;Peter M. VanNostrand;Huayi Zhang;Yizhou Yan;Lei Cao;S. Madden;Elke A. Rundensteiner
通讯作者：
Dennis M. Hofmann;Peter M. VanNostrand;Huayi Zhang;Yizhou Yan;Lei Cao;S. Madden;Elke A. Rundensteiner

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
期刊：
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
影响因子：
0
作者：
Zhang, Huayi;Cao, Lei;VanNostrand, Peter;Madden, Samuel;Rundensteiner, Elke A.
通讯作者：
Rundensteiner, Elke A.

AutoOD: Automatic Outlier Detection