图 书 馆
SpotLight：检测流图中的异常SpotLight: Detecting Anomalies in Streaming Graphs |
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| 课程网址: | http://videolectures.net/kdd2018_eswaran_detecting_anomalies/ |
| 主讲教师: | Dhivya Eswaran |
| 开课单位: | 卡内基梅隆大学 |
| 开课时间: | 2018-11-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | How do we spot interesting events from e-mail or transportation logs? How can we detect port scan or denial of service attacks from IP-IP communication data? In general, given a sequence of weighted, directed or bipartite graphs, each summarizing a snapshot of activity in a time window, how can we spot anomalous graphs containing the sudden appearance or disappearance of large dense subgraphs (e.g., near bicliques) in near real-time using sublinear memory? To this end, we propose a randomized sketching-based approach called SpotLight, which guarantees that an anomalous graph is mapped ‘far’ away from ‘normal’ instances in the sketch space with high probability for appropriate choice of parameters. Extensive experiments on real-world datasets show that SpotLight (a) improves accuracy by at least 8.4% compared to prior approaches, (b) is fast and can process millions of edges within a few minutes, (c) scales linearly with the number of edges and sketching dimensions and (d) leads to interesting discoveries in practice. |
| 关 键 词: | SpotLight; 检测流图中的异常; 从电子邮件或交通日志中挖掘; 真实世界数据集 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-09：cyh |
| 最后编审: | 2023-05-15：cyh |
| 阅读次数: | 19 |