Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a...
在DeepTraLog 中,A trace由 TEG 表示,它是一个有向属性图g = \{ , , \} 其中: V 是一组nodes(即events); A 是图的邻接矩阵;X \in R^{|V| \cdot d}是节点属性矩阵, 其中X的每一行x_v是节点v \in V的属性,(即event vector),d是事件向量的维数。 GGNN 将图中的nodes表示为神经网络的单元u...
1. Anomalous Edge Detection dynamic graphs, anomaly detection in dynamic graph (Add-Graph) 将GCN和GRU一起使用,GCN从图形中提取特征,GRU捕获对异常检测有用的历史信息。能够在窗口中集成长期和短期模式,以便通过使用结构、属性和时间信息来描述正常边缘。具体而言,它利用GCN通过利用节点的结构和属性特征来处理当前...
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As objects in graphshave long-range correlations, a suite of novel technology has been developedfor anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overviewof the state-of-the-art methods for anomaly detection in data represented asgraphs. As...
Because brain discharge is quite different with and without a seizure, the authors considered the seizure interval as an abnormal time interval and proposed a graph-based detection approach. In this case, detecting this type of anomalies can help experts analyze and understand the patterns of ...
In this paper, we use variational recurrent neural network to investigate the anomaly detection problem on graph time series. The temporal correlation is modeled by the combination of recurrent neural network (RNN) and variational inference (VI), while the spatial information is captured by the grap...
[Python] NAB: The Numenta Anomaly Benchmark: NAB is a novel benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. [Python] CueObserve: Anomaly detection on SQL data warehouses and databases. [Python] Chaos Genius: ML powered analytics engine for outlier/...
anomaly detection algorithms, one-class support vector machine has been widely used to detect outliers. However, those traditional anomaly detection methods lost their effectiveness in graph data. Since traditional anomaly detection methods are stable, robust and easy to use, it is vitally important ...
Therefore, in this paper, we design a GCN (graph convolutional networks) based anomaly detection model to detect anomalous behaviors of users and malicious threat groups. The GCN model could characterize entities' properties and structural information between them into graphs. This allows the GCN ...