Bringing data analytics to the network nodes for efficient traffic anomalies detection
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Document typeConference report
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessRestricted access - publisher's policy
European Commission's projectMETRO-HAUL - METRO High bandwidth, 5G Application-aware optical network, with edge storage, compUte and low Latency (EC-H2020-761727)
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.
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