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Bringing data analytics to the network nodes for efficient traffic anomalies detection

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10.1109/ICTON.2017.8024981
 
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hdl:2117/113167

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Pérez Vela, AlbaMés informació
Ruiz Ramírez, MarcMés informacióMés informacióMés informació
Velasco Esteban, Luis DomingoMés informacióMés informacióMés informació
Document typeConference report
Defense date2017
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectMETRO-HAUL - METRO High bandwidth, 5G Application-aware optical network, with edge storage, compUte and low Latency (EC-H2020-761727)
Abstract
Traffic anomalies can create network congestion, so its prompt and accurate detection would allow network operators to make decisions to guarantee the network performance avoiding services to experience any perturbation. In this paper, we focus on origin-destination (OD) traffic anomalies; to efficiently detect those, we study two different anomaly detection methods based on data analytics and combine them with three monitoring strategies. In view of the short monitoring period needed to reduce anomaly detection, which entails large amount of monitoring data to be collected and analyzed in a centralized repository, we propose bringing data analytics to the network nodes to efficiently detect traffic anomalies, while keeping traffic estimation centralized. Exhaustive simulation results on a realistic network scenario show that the monitoring period should be as low as possible (e.g., 1 min) to keep anomaly detection times low, which clearly motivates to place traffic anomaly detection function in the network nodes.
CitationP. Vela, Alba, Ruiz, M., Velasco, L. Bringing data analytics to the network nodes for efficient traffic anomalies detection. A: International Conference on Transparent Optical Networks. "ICTON 2017: 19th International Conference on Transparent Optical Networks: Girona, Catalonia, Spain, 2-6 July 2017". Girona: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1-4. 
URIhttp://hdl.handle.net/2117/113167
DOI10.1109/ICTON.2017.8024981
ISBN978-1-5386-0858-6
Publisher versionhttp://ieeexplore.ieee.org/document/8024981/?reload=true
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  • GCO - Grup de Comunicacions Òptiques - Ponències/Comunicacions de congressos [402]
  • Departament d'Arquitectura de Computadors - Ponències/Comunicacions de congressos [1.827]
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