LSTM-based radiography for anomaly detection in softwarized infrastructures - Archive ouverte HAL Access content directly
Conference Papers Year : 2020

LSTM-based radiography for anomaly detection in softwarized infrastructures

Abstract

Legacy and novel network services are expected to be migrated and designed to be deployed in fully virtualized environments. Starting with 5G, NFV becomes a formally required brick in the specifications, for services integrated within the infrastructure provider networks. This evolution leads to deployment of virtual resources Virtual-Machine (VM)-based, container-based and/or server-less platforms, all calling for a deep virtualization of infrastructure components. Such a network softwarization also unleashes further logical network virtualization, easing multi-layered, multi-actor and multi-access services, so as to be able to fulfill high availability, security, privacy and resilience requirements. However, the derived increased components heterogeneity makes the detection and the characterization of anomalies difficult, hence the relationship between anomaly detection and corresponding reconfiguration of the NFV stack to mitigate anomalies. In this article we propose an unsupervised machine-learning data-driven approach based on Long-Short-Term-Memory (LSTM) autoencoders to detect and characterize anomalies in virtualized networking services. With a radiography visualization, this approach can spot and describe deviations from nominal parameter values of any virtualized network service by means of a lightweight and iterative mean-squared reconstruction error analysis of LSTM-based autoencoders. We implement and validate the proposed methodology through experimental tests on a vIMS proof-of-concept deployed using Kubernetes.
Fichier principal
Vignette du fichier
_A_Machine_Learning_Methology_to_Characterize_Anomalies_in_Softwarized_Networks-6.pdf (1.38 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02917660 , version 1 (19-08-2020)

Identifiers

Cite

Alessio Diamanti, José Manuel Sanchez Vilchez, Stefano Secci. LSTM-based radiography for anomaly detection in softwarized infrastructures. International Teletraffic Congress, IEEE, Sep 2020, Osaka, Japan. ⟨10.1109/ITC3249928.2020.00012⟩. ⟨hal-02917660⟩
359 View
330 Download

Altmetric

Share

Gmail Facebook X LinkedIn More