Self-Diagnosis Technique for Virtual Private Networks Combining Bayesian Networks and Case-Based Reasoning

Bennacer, Leila; Amirat, Yacine; Chibani, Abdelghani; Mellouk, Abdelhamid; Ciavaglia, Laurent

doi:10.1109/tase.2014.2321011

Cited by 68 publications

(40 citation statements)

References 23 publications

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“…Bayesian networks are a natural candidate to perform inference, and case‐based reasoning is another valuable technique that helps to exploit previous expert knowledge on the domain. Both techniques have been proposed for RCA …”

Section: Machine Learning For Anomaly Detection and Rcamentioning

confidence: 99%

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A Big Data and machine learning approach for network monitoring and security

Maccari

Passerini

2018

Security and Privacy

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In the last decade the performances of 802.11 (Wi‐Fi) devices skyrocketed. Today it is possible to realize gigabit wireless links spanning across kilometers at a fraction of the cost of the wired equivalent. In the same period, mesh network evolved from being experimental tools confined into university labs, to systems running in several real world scenarios. Mesh networks can now provide city‐wide coverage and can compete on the market of Internet access. Yet, being wireless distributed networks, mesh networks are still hard to maintain and monitor. This paper explains how today we can perform monitoring, anomaly detection and root cause analysis in mesh networks using Big Data techniques. It first describes the architecture of a modern mesh network, it justifies the use of Big Data techniques and provides a design for the storage and analysis of Big Data produced by a large‐scale mesh network. While proposing a generic infrastructure, we focus on its application in the security domain.

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Section: Machine Learning For Anomaly Detection and Rcamentioning

confidence: 99%

“…Both techniques have been proposed for RCA. 4,46 Figure 8 represents a possible structure for a Bayesian network representing a toy communication network (we limit the analysis to the Phy, MAC, and IP layer for clarity). In the figure we show how in each node the physical layer influences the MAC, which in turn influences the IP.…”

Section: Root Cause Analysismentioning

confidence: 99%

A Big Data and machine learning approach for network monitoring and security

Maccari

Passerini

2018

Security and Privacy

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“…Update the velocity vectors' elements of the bats according to equation (4) and equations (8)- (13), and update the position vectors' elements of the bats according to the v-shaped transfer function and position updating rule given by equations (6) and (7).…”

Section: Feimentioning

confidence: 99%

Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization

Fei

2017

Advances in Mechanical Engineering

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In this article, fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm is proposed, and the improved binary bat algorithm is used to select the appropriate features and kernel parameter of relevance vector machine. In the improved binary bat algorithm, the new velocities updating method of the bats is presented in order to ensure the decreasing of the probabilities of changing their position vectors' elements when the position vectors' elements of the bats are equal to the current best location's element, and the increasing of the probabilities of changing their position vectors' elements when the position vectors' elements of the bats are unequal to the current best location's element, which are helpful to strengthen the optimization ability of binary bat algorithm. The traditional relevance vector machine trained by the training samples with the unreduced features can be used to compare with the proposed improved binary bat algorithm-relevance vector machine method. The experimental results indicate that improved binary bat algorithm-relevance vector machine has a stronger fault diagnosis ability of bearing than the traditional relevance vector machine trained by the training samples with the unreduced features, and fault diagnosis of bearing based on improved binary bat algorithm-relevance vector machine is feasible.

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“…In order to reduce the computation intractability associated with the use of Bayesian networks for large number of nodes, a hybrid approach is proposed by Bennacer et al in [30] that combines the use of Bayesian networks (BN) and case-based reasoning (CBR). The basic idea of this approach is to use CBR for simplifying and optimizing the fault diagnosis process by reducing the inherent complexity associated with the BNbased fault diagnosis, and also retain the advantages associated with the use of BN.…”

Section: Passive Monitoring Techniquesmentioning

confidence: 99%

Recent Advances in Fault Localization in Computer Networks

Dusia

Sethi

2016

IEEE Commun. Surv. Tutorials

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Fault localization, a core element in network fault management, is the process of inferring the exact failure in a network from the set of observed symptoms. Since faults in network systems can be unavoidable, their quick and accurate detection and diagnosis is important for the stability, consistency, and performance of a communication system. In this paper, we discuss the challenges of fault localization in complex communication systems and present an overview of recent techniques proposed in the literature along with their advantages and limitations. We start by briefly surveying passive monitoring techniques which were previously reviewed in a survey by Steinder [1]. We then describe more recent fault localization research in five categories: active monitoring techniques, techniques for overlay and virtual networks, decentralized probabilistic management techniques, temporal correlation techniques, and learning techniques.

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Self-Diagnosis Technique for Virtual Private Networks Combining Bayesian Networks and Case-Based Reasoning

Cited by 68 publications

References 23 publications

A Big Data and machine learning approach for network monitoring and security

A Big Data and machine learning approach for network monitoring and security

Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization

Recent Advances in Fault Localization in Computer Networks

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