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Anomaly detection in complex trading systems

机译：复杂交易系统中的异常检测

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System availability is one of the major requirements expected from systems in the trading domain. In order to prevent system outages that can deteriorate system availability, anomaly detection must be able to assess the status of the system and detect anomalies that can lead to failures on a real-time basis. This paper presents a framework for anomaly detection for complex trading systems based on supervised learning approaches. Multiple feature reduction techniques were experimented with, in order to eliminate the noisy features that were initially derived from the system parameters. A classification technique based on Radial Basis Function (RBF) kernel Support Vector Machine (SVM) along with a feature selection technique built on a tree-based ensemble displayed the most promising results.
机译：系统可用性是交易域中的系统预期的主要要求之一。为了防止系统中断可以恶化系统可用性，异常检测必须能够评估系统的状态并检测可能在实时导致故障的异常。本文介绍了基于监督学习方法的复杂交易系统的异常检测框架。尝试多种特征减少技术，以消除最初从系统参数衍生的嘈杂特征。一种基于径向基函数（RBF）内核支持向量机（SVM）的分类技术以及基于树的集合中构建的特征选择技术显示了最有前途的结果。

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