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Conference papers

Nadege: When Graph Kernels meet Network Anomaly Detection

Hicham Lesfari 1, 2, 3 Frédéric Giroire 3, 2, 1 
1 COATI - Combinatorics, Optimization and Algorithms for Telecommunications
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - COMRED - COMmunications, Réseaux, systèmes Embarqués et Distribués
Abstract : With the continuous growing level of dynamicity, heterogeneity, and complexity of traffic data, anomaly detection remains one of the most critical tasks to ensure an efficient and flexible management of a network. Recently, driven by their empirical success in many domains, especially bioinformatics and computer vision, graph kernels have attracted increasing attention. Our work aims at investigating their discrimination power for detecting vulnerabilities and distilling traffic in the field of networking. In this paper, we propose Nadege, a new graph-based learning framework which aims at preventing anomalies from disrupting the network while providing assistance for traffic monitoring. Specifically, we design a graph kernel tailored for network profiling by leveraging propagation schemes which regularly adapt to contextual patterns. Moreover, we provide provably efficient algorithms and consider both offline and online detection policies. Finally, we demonstrate the potential of kernel-based models by conducting extensive experiments on a wide variety of network environments. Under different usage scenarios, Nadege significantly outperforms all baseline approaches.
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Contributor : Frédéric Giroire Connect in order to contact the contributor
Submitted on : Saturday, April 30, 2022 - 10:51:18 AM
Last modification on : Tuesday, May 3, 2022 - 3:46:45 AM


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  • HAL Id : hal-03655867, version 1


Hicham Lesfari, Frédéric Giroire. Nadege: When Graph Kernels meet Network Anomaly Detection. IEEE International Conference on Computer Communications (INFOCOM), May 2022, London, United Kingdom. ⟨hal-03655867⟩



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