Learning and Adapting to Spatio-Temporal Anomalies

学习和适应时空异常

• 批准号: 0830490
• 负责人: Clayton Scott
• 金额: $ 56.43万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0830490&HistoricalAwards=false
• 关键词: Learning; Adapting; Spatio; Temporal; Anomalies

Anomaly detection is essential for a broad range of security, surveillance, and monitoring problems in areas ranging from health care and environmental protection to homeland security and manufacturing. However, because of the increasing complexity of systems and data, as well as the increasing sophistication of adversaries, traditional methods of anomaly detection are no longer sufficient. These methods assume that anomalies look substantially different from normal measurements, or that their characteristics remain constant over time. In many practical applications of interest, however, anomalies are only distinguished by subtle spatio-temporal characteristics. For example, a network security event may involve a sequence of traffic patterns, each of which is innocuous in its own right, but which occupy a localized or lower-dimensional spatial domain when viewed together. Alternatively, anomalies may exhibit temporal structure caused by a slow but steady drift from normal to abnormal behavior. This project develops new theoretical and algorithmic approaches to detecting such spatio-temporal anomalies. The research will impact the monitoring of a wide range of critical infrastructures and application domains, including environmental systems, health care networks, power grids, and communication networks.The project focuses on spatio-temporal anomalies that (1) have significant spatial overlap with the nominal distribution, (2) are distributed on a manifold of lower dimension than nominal measurements, and (3) have time-varying distributional characteristics. The research applies and extends techniques from statistical machine learning, including transductive, manifold-adaptive, and online learning, to the anomaly detection setting. This framework allows the investigators to address several difficult and long-standing challenges such as optimal tracking of drifting anomaly distributions and efficient relaxations of combinatorial graph-based inference algorithms.

异常检测对于从医疗保健和环境保护到国土安全和制造业等领域的广泛安全，监视和监控问题至关重要。然而，由于系统和数据的日益复杂，以及对手的日益复杂，传统的异常检测方法不再足够。这些方法假设异常看起来与正常测量有很大不同，或者它们的特征随时间保持不变。然而，在许多感兴趣的实际应用中，异常仅通过细微的时空特征来区分。例如，网络安全事件可能涉及一系列流量模式，每个流量模式本身都是无害的，但是当一起查看时，它们占据局部或低维空间域。或者，异常可能表现出由从正常到异常行为的缓慢但稳定的漂移引起的时间结构。该项目开发了新的理论和算法方法来检测这种时空异常。该研究将影响广泛的关键基础设施和应用领域的监测，包括环境系统，医疗保健网络，电网和通信网络。该项目侧重于时空异常，（1）与标称分布有显着的空间重叠，（2）分布在比标称测量低维的流形上，（3）分布在比标称测量低维的流形上，（4）分布在比标称测量低维的流形上，（5）分布在比标称测量低维的流形上。（3）具有时变分布特征。该研究应用并扩展了从统计机器学习（包括转导，流形自适应和在线学习）到异常检测设置的技术。该框架允许研究人员解决几个困难和长期存在的挑战，如漂移异常分布的最佳跟踪和基于图的组合推理算法的有效放松。