Collaborative Research: ELEMENTS: Tuning-free Anomaly Detection Service

在许多应用程序中，发现和了解数据中的异常行为非常重要。存在大量异常检测算法，可能很难确定哪种算法最适合特定领域。一旦选择了算法，用户必须手动调整许多参数才能使算法运行良好；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非监督异常检测技术，探索识别最佳匹配的选项，并以比手动调整更高的精度对异常进行分类。该项目将在数据集上自动测试一系列非监督异常技术，从组合检测结果中提取知识以可靠地区分异常和正常数据，并将这些知识用作标签来训练异常分类器；目标是以高于彻底手动调整所能达到的精度对异常进行分类。该方法可以应用于各种数据类型和域。由此产生的网络基础设施提供了无需调整的异常检测功能，同时使纳入特定于领域的要求变得容易。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。对非监督异常检测包的评估将使用数据集，并与麻省总医院/哈佛医学院、网络安全研究和Signify(前身为飞利浦照明)的合作者建立合作伙伴关系，以确保在整个开发过程中验证包的实用性和可用性。这项由高级网络基础设施办公室颁发的奖项由计算机和信息科学与工程局内的NSF信息和智能系统部门共同支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.