Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning
Anomaly Detection in Dynamic Networks using Multi-view Time-Series Hypersphere Learning
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DOI：
10.1145/3132847.3132964
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发表时间：
2017-11
期刊：
影响因子：
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通讯作者：
Xian Teng;Y. Lin;Xidao Wen
中科院分区：
文献类型：
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作者：
Xian Teng;Y. Lin;Xidao Wen
由于时间动态的复杂性和多数据源反映的变化，从动态和多属性网络系统中检测异常模式一直是一个具有挑战性的问题。我们提出了一种多视图时间序列超球学习（MTHL）方法，利用多视图学习和支持向量描述来解决这个问题。给定一个具有时变边和节点属性的动态网络，MTHL将多视点时间序列数据投影到一个共享的潜在子空间中，然后学习一个围绕正常样本的具有软约束的紧致超球面.学习的超球体允许有效地区分正常和异常情况。我们进一步提出了一个有效的，两阶段交替优化算法作为MTHL的解决方案。在合成和真实的数据集上进行了大量的实验。结果表明，我们的方法在检测三种类型的事件方面优于最先进的基线方法，这三种类型的事件涉及（i）单独的时变特征，（ii）单独的时间聚合特征，以及（iii）两种特征。此外，我们的方法表现出一致的和良好的性能，面对的问题，包括噪音，异常污染的训练阶段和数据不平衡。
Detecting anomalous patterns from dynamic and multi-attributed network systems has been a challenging problem due to the complication of temporal dynamics and the variations reflected in multiple data sources. We propose a Multi-view Time-Series Hypersphere Learning (MTHL) approach that leverages multi-view learning and support vector description to tackle this problem. Given a dynamic network with time-varying edge and node properties, MTHL projects multi-view time-series data into a shared latent subspace, and then learns a compact hypersphere surrounding normal samples with soft constraints. The learned hypersphere allows for effectively distinguishing normal and abnormal cases. We further propose an efficient, two-stage alternating optimization algorithm as a solution to the MTHL. Extensive experiments are conducted on both synthetic and real datasets. Results demonstrate that our method outperforms the state-of-the-art baseline methods in detecting three types of events that involve (i) time-varying features alone, (ii) time-aggregated features alone, as well as (iii) both features. Moreover, our approach exhibits consistent and good performance in face of issues including noises, anomaly pollution in training phase and data imbalance.