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通过分数分解生成网络异常的局部解释

Generating Local Explanations of Network Anomalies via Score Decomposition
课程网址: https://videolectures.net/videos/kdd2016_la_fond_score_decomposit...  
主讲教师: Timothy La Fond
开课单位: KDD 2016研讨会
开课时间: 2025-02-04
课程语种: 英语
中文简介:
网络分析中的一个重要应用是检测网络时间序列中的异常事件。这些事件可能只是网络时间线中感兴趣的时间,也可能是恶意活动或网络故障的例子。一旦异常检测算法识别出一组事件,在这些时候对图进行更详细的检查可以揭示网络行为的重要细节。在本文中,我们使用几个动态网络中报告的异常的全局异常得分的得分分解来识别最异常行为的区域,并对异常事件的性质提供解释。我们还定义了一个新版本的图形编辑距离和聚类系数统计,它更善于找到异常行为的局部解释。
课程简介: An important application in network analysis is the detection of anomalous events in a network time series. These events could merely be times of interest in the network timeline or they could be examples of malicious activity or network malfunction. Once a set of events are identified by the anomaly detection algorithm, a more detailed examination of the graph at these times can reveal important details about the behavior of the network. In this paper we use the score decomposition of the global anomaly score of reported anomalies in several dynamic networks to identify the regions of most anomalous behavior and provide interpretations as to the nature of the anomalous events. We also define a new version of the Graph Edit Distance and Clustering Coefficient statistics which are better at finding the local explanations for anomalous behavior.
关 键 词: 分数分解; 网络异常; 局部解释
课程来源: 视频讲座网
数据采集: 2025-03-01:liyq
最后编审: 2025-03-01:liyq
阅读次数: 5