III: Small: Collaborative Research: A novel paradigm for detecting complex anomalous patterns in multi-modal, heterogeneous, and high-dimensional multi-source data sets

III：小型：协作研究：一种检测多模态、异构和高维多源数据集中复杂异常模式的新范式

• 批准号: 1954409
• 负责人: Feng Chen
• 金额: $ 20.59万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1954409&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; novel

One of the greatest challenges in modern data analysis is to identify subtle, complex anomalous patterns (subsets of a data set that are novel or unexpected) within ubiquitous multi-modal, heterogeneous, and high-dimensional multi-source data sets in the current big data era. The detection of such salient patterns is an indispensable tool for knowledge mining and discovery in important applications across many fields of science, engineering, and business, including the early detection of infectious disease outbreaks, crime hotspots, network intrusions, false advertising, cyber botnets, customer activity monitoring and user profiling, and fraudulent medical claims, among others. The project research goal is to develop a new and innovative paradigm for discovering complex and subtle anomalous patterns in ubiquitous multi-modal, heterogeneous, and high-dimensional multi-source datasets in the current big data era. The key idea is to generalize the idea of meta-analysis from the statistical community and to reframe the problem as a search over all subsets of nonparametric statistical tests that are conducted on individual record-level features, in order to find the subsets (anomalous patterns) that are jointly significant. The project is focused on real-world problems related to biosurveillance and cybersecurity with two challenging applications: early detection of rare and infectious disease outbreaks (e.g., foodborne, Hantavirus, yellow fever) and Sybil attacks (e.g., spammers, fake users, and compromised normal users). The integrated education plan includes the development of new courses offered at the Master of Science program in Computer Science and Informatics and outreach to underrepresented groups. The outcomes of this project will be widely disseminated to broader audience via tutorials and workshops.The research objectives of this project are: (1) the development of nonparametric tests for modeling anomalous information of multi-source datasets; (2) learning heterogeneous dependencies among nonparametric tests; (3) detecting anomalous patterns from an extremely large set of nonparametric tests; and (4) making the detected anomalous patterns interpretable in the context of multi-modal, heterogeneous, and high-dimensional data. The research approach includes the development of (1) nonparametric tests on individual record level features that provide consistent representations of anomalous information from multiple heterogeneous input modalities, such as image, text, video, and multiple sensor streams; (2) deep structured and adversarial methods capable of learning robust hierarchical dependency structures of nonparametric tests using unlabeled training data; (3) fast, scalable combinatorial optimization methods capable of accurately detecting salient anomalous patterns from billion-size nonparametric tests; and (4) transparent and interpretable methods capable of explaining the predicted anomalous patterns by identifying training instances and features that are most responsible for the predictions.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.

现代数据分析面临的最大挑战之一是在当前大数据中普遍存在的多模态、异构和高维多源数据集中识别微妙、复杂的异常模式（新颖或意外的数据集子集）。数据时代。对此类显着模式的检测是科学、工程和商业许多领域重要应用中知识挖掘和发现不可或缺的工具，包括早期发现传染病爆发、犯罪热点、网络入侵、虚假广告、网络僵尸网络、客户活动监控和用户分析以及欺诈性医疗索赔等。该项目的研究目标是开发一种新的创新范式，用于发现当前大数据时代普遍存在的多模态、异构、高维多源数据集中复杂而微妙的异常模式。关键思想是概括统计界的元分析思想，并将问题重新构建为对对单个记录级特征进行的非参数统计测试的所有子集的搜索，以便找到子集（异常模式） ）共同重要。该项目专注于与生物监测和网络安全相关的现实世界问题，具有两个具有挑战性的应用：早期检测罕见和传染病爆发（例如食源性、汉坦病毒、黄热病）和女巫攻击（例如垃圾邮件发送者、虚假用户和受损的病毒）普通用户）。综合教育计划包括开发计算机科学和信息学理学硕士课程提供的新课程，以及向代表性不足的群体进行推广。该项目的成果将通过教程和研讨会向更广泛的受众广泛传播。该项目的研究目标是：（1）开发用于建模多源数据集异常信息的非参数检验； (2)学习非参数检验之间的异构依赖关系； (3) 从大量非参数检验中检测异常模式； (4) 使检测到的异常模式在多模态、异构和高维数据的背景下可以解释。研究方法包括开发（1）对单个记录级特征的非参数测试，这些特征提供来自多种异构输入模式（例如图像、文本、视频和多个传感器流）的异常信息的一致表示； （2）深层结构化和对抗性方法，能够使用未标记的训练数据学习非参数测试的鲁棒分层依赖结构； (3) 快速、可扩展的组合优化方法，能够从数十亿规模的非参数测试中准确检测显着的异常模式； (4) 透明且可解释的方法，能够通过识别对预测最有影响的训练实例和特征来解释预测的异常模式。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和能力进行评估，被认为值得支持。更广泛的影响审查标准。

项目成果

Block-Structured Optimization for Anomalous Pattern Detection in Interdependent Networks

• DOI: 10.1109/icdm.2019.00137
• 发表时间: 2019-11
• 期刊: 2019 IEEE International Conference on Data Mining (ICDM)
• 作者: Fei Jie;Chunpai Wang;Feng Chen;Lei Li;Xindong Wu

A Primal-Dual Subgradient Approach for Fair Meta Learning

• DOI: 10.1109/icdm50108.2020.00091
• 发表时间: 2020-11
• 期刊: 2020 IEEE International Conference on Data Mining (ICDM)
• 作者: Chengli Zhao;Feng Chen;Zhuoyi Wang;L. Khan

Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning

• 发表时间: 2021
• 作者: Hitesh Sapkota;Yiming Ying;F. Chen;Qi Yu

Calibrated Nonparametric Scan Statistics for Anomalous Pattern Detection in Graphs

用于图形中异常模式检测的校准非参数扫描统计

• 发表时间: 2022
• 期刊: Thirty-Sixth AAAI Conference on Artificial Intelligence
• 作者: Chunpai Wang, Daniel Neill

Fairness-Aware Online Meta-learning

公平意识在线元学习

• 发表时间: 2021
• 期刊: In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
• 作者: Zhao, Chen;Chen, Feng;Thuraisingham, Bhavani
• 通讯作者: Thuraisingham, Bhavani