Anomalous Model-Driven-Telemetry Network-Stream BGP Detection

Fezeu, Rostand A. K.

doi:10.1109/icnp49622.2020.9259411

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…The Network Telemetry Framework (NTF) [20] is a standardization effort to enable real-time and fine-grained network monitoring for autonomic networks [21], [22]. An example of application of this concept is presented in [23]. In [24] the authors present the results of experiments on programmable telemetry.…”

Section: Related Workmentioning

confidence: 99%

Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring

Camacho,

Wasielewska,

Bro

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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There is an increasing interest in the development of new data-driven models useful to assess the performance of communication networks. For many applications, like network monitoring and troubleshooting, a data model is of little use if it cannot be interpreted by a human operator. In this paper, we present an extension of the Multivariate Big Data Analysis (MBDA) methodology, a recently proposed interpretable data analysis tool. In this extension, we propose a solution to the automatic derivation of features, a cornerstone step for the application of MBDA when the amount of data is massive. The resulting network monitoring approach allows us to detect and diagnose disparate network anomalies, with a data-analysis workflow that combines the advantages of interpretable and interactive models with the power of parallel processing. We apply the extended MBDA to two case studies: UGR'16, a benchmark flow-based real-traffic dataset for anomaly detection, and Dartmouth'18, the longest and largest Wi-Fi trace known to date.

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Section: Related Workmentioning

confidence: 99%

Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring

Camacho,

Wasielewska,

Bro

et al. 2024

IEEE Trans. Netw. Serv. Manage.

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“…MDT provides efficient mechanisms for subscribing to specific YANG data in a model and for pushing the data, either periodically or upon changes, to data collectors. In this sense, MDT enables near real-time monitoring of a network infrastructure, for example, by dynamically detecting a shutdown of an interface, or by identifying network anomalies based on BGP information [2]. For these reasons, YANG data models, and consequently MDT, are considered key enablers for network monitoring automation [3].…”

Section: Introductionmentioning

confidence: 99%

Toward Building a Semantic Network Inventory for Model-Driven Telemetry

et al. 2023

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Network telemetry based on data models is expected to become the standard mechanism for collecting operational data from network devices efficiently. But the wide variety of standard and proprietary data models along with the different implementations of telemetry protocols offered by network vendors, become a barrier when monitoring heterogeneous network infrastructures. To facilitate the integration and sharing of context information related to model-driven telemetry, this work proposes a semantic network inventory that integrates new information models specifically developed to capture context information in a vendor-agnostic fashion using current standards defined for context management. To automate the integration of this context information within the network inventory, a reference architecture is designed. Finally, a prototype of the solution is implemented and validated through a case study that illustrates how the network inventory can ease the operation of modeldriven telemetry in multi-vendor networks.

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