Anomaly Detection
Anomaly Detection
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DOI：
10.1145/1541880.1541882
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发表时间：
2009-11-01
影响因子：
3.1
通讯作者：
Lu, Thomas T.
中科院分区：
文献类型：
--
作者：
Prasad, Nadipuram R.;Almanza-Garcia, Salvador;Lu, Thomas T.
本文提出了一个革命性的框架，用于建模，检测，表征，识别和机器学习的异常行为所观察到的现象所产生的一大类未知和不确定的动力系统。一个进化的行为通常是很难纠正的，除非导致这种行为的特定异常事件可以被早期检测到，并且在检测到特定异常之后归因于特定异常的任何后果。需要大量的调查时间和精力来追溯异常行为的原因，并重新创建导致这种异常行为的事件序列。因此，使用状态运动原理自动检测异常行为的需求至关重要，并且需要在循环中使用人类操作员来完成。人机交互导致机器自我学习的能力，并产生强大的决策支持机制。这是智能控制的基本概念，其中机器学习通过与人类操作员的交互来增强。
The paper presents a revolutionary framework for the modeling, detection, characterization, identification, and machine-learning of anomalous behavior in observed phenomena arising from a large class of unknown and uncertain dynamical systems. An evolved behavior would in general be very difficult to correct unless the specific anomalous event that caused such behavior can be detected early, and any consequence attributed to the specific anomaly following its detection. Substantial investigative time and effort is required to back-track the cause for abnormal behavior and to recreate the event sequence leading to such abnormal behavior. The need to automatically detect anomalous behavior is therefore critical using principles of state motion, and to do so with a human operator in the loop. Human-machine interaction results in a capability for machine self-learning and in producing a robust decision-support mechanism. This is the fundamental concept of intelligent control wherein machine-learning is enhanced by interaction with human operators.