DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F16%3A00091413" target="_blank" >RIV/00216224:14330/16:00091413 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-46349-0_27" target="_blank" >http://dx.doi.org/10.1007/978-3-319-46349-0_27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-46349-0_27" target="_blank" >10.1007/978-3-319-46349-0_27</a>

Jazyk výsledku

angličtina

Název v původním jazyce

DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs

Popis výsledku v původním jazyce

Ubiquitous network data has given rise to diverse graph mining and analytical methods. One of the graph mining domains is anomaly detection in dynamic graphs, which can be employed for fraud detection, network intrusion detection, suspicious behaviour identification, etc. Most existing methods search for anomalies rather on the global level of the graphs. In this work, we propose a new anomaly detection and explanation algorithm for dynamic graphs. The algorithm searches for anomaly patterns in the form of predictive rules that enable us to examine the evolution of dynamic graphs on the level of subgraphs. Specifically, these patterns are able to capture addition and deletion of vertices and edges, and relabeling of vertices and edges. In addition, the algorithm outputs normal patterns that serve as an explanation for the anomaly patterns. The algorithm has been evaluated on two real-world datasets.

Název v anglickém jazyce

DGRMiner: Anomaly Detection and Explanation in Dynamic Graphs

Popis výsledku anglicky

Ubiquitous network data has given rise to diverse graph mining and analytical methods. One of the graph mining domains is anomaly detection in dynamic graphs, which can be employed for fraud detection, network intrusion detection, suspicious behaviour identification, etc. Most existing methods search for anomalies rather on the global level of the graphs. In this work, we propose a new anomaly detection and explanation algorithm for dynamic graphs. The algorithm searches for anomaly patterns in the form of predictive rules that enable us to examine the evolution of dynamic graphs on the level of subgraphs. Specifically, these patterns are able to capture addition and deletion of vertices and edges, and relabeling of vertices and edges. In addition, the algorithm outputs normal patterns that serve as an explanation for the anomaly patterns. The algorithm has been evaluated on two real-world datasets.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Data Analysis XV - 15th International Symposium, IDA 2016

ISBN

9783319463483

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

308-319

Název nakladatele

Springer

Místo vydání

Neuveden

Místo konání akce

Stockholm, Sweden

Datum konání akce

13. 10. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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