R
Romain Fontugne
Researcher at National Institute of Informatics
Publications - 48
Citations - 1115
Romain Fontugne is an academic researcher from National Institute of Informatics. The author has contributed to research in topics: Anomaly detection & The Internet. The author has an hindex of 14, co-authored 48 publications receiving 894 citations. Previous affiliations of Romain Fontugne include University of Tokyo & Graduate University for Advanced Studies.
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Proceedings ArticleDOI
MAWILab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking
TL;DR: The goal of the present article is to assist researchers in the evaluation of detectors by providing them with labeled anomaly traffic traces by proposing a reliable graph-based methodology that combines any anomaly detector outputs.
Journal ArticleDOI
Scaling in Internet Traffic: A 14 Year and 3 Day Longitudinal Study, With Multiscale Analyses and Random Projections
Romain Fontugne,Patrice Abry,Kensuke Fukuda,Darryl Veitch,Kenjiro Cho,Pierre Borgnat,Herwig Wendt +6 more
TL;DR: In this paper, the authors present a methodology combining multiscale analysis (wavelet and wavelet leaders) and random projections (or sketches), permitting a precise, efficient and robust characterization of scaling.
Proceedings ArticleDOI
Strip, bind, and search: a method for identifying abnormal energy consumption in buildings
Romain Fontugne,Jorge Ortiz,Nicolas Tremblay,Pierre Borgnat,Patrick Flandrin,Kensuke Fukuda,David E. Culler,Hiroshi Esaki +7 more
TL;DR: A new approach called the Strip, Bind and Search (SBS) is presented; a method for uncovering abnormal equipment behavior and in-concert usage patterns that uncovers misbehavior corresponding to inefficient device usage that leads to energy waste.
Proceedings ArticleDOI
Hashdoop: A MapReduce framework for network anomaly detection
TL;DR: Hashdoop is proposed, a MapReduce framework that splits traffic with a hash function to preserve traffic structures and, hence, profits of distributed computing infrastructures to detect network anomalies.
Proceedings ArticleDOI
A taxonomy of anomalies in backbone network traffic
TL;DR: A new taxonomy of network anomalies with wide coverage of existing work is presented and a set of signatures that assign taxonomy labels to events are provided that provide new insights regarding events previous classified by heuristic rule labeling.