Application of AI to Network Operation

Kawahara, Ryoichi; Watanabe, Keishiro; Harada, Shinji; Kawata, Takehiro

doi:10.1587/bplus.12.29

Cited by 5 publications

(5 citation statements)

References 25 publications

(8 reference statements)

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“…NFV works in a virtualized environment that has various vulnerabilities. We categorized it into two main types, performance-related vulnerabilities and security-related vulnerabilities [33]. Performance-related vulnerabilities occur due to weakness in the network architecture, lack of data flow control and backup devices, the poor configuration of software and security devices, etc.…”

Section: Nfv Security Issuesmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey

Zehra,

Faseeha,

Syed

et al. 2023

Sensors

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Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.

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Section: Nfv Security Issuesmentioning

confidence: 99%

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey

Zehra,

Faseeha,

Syed

et al. 2023

Sensors

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“…NTT Network Technology Laboratories is making headway in the development of an autoencoder (AE)based network anomaly detection system designed to achieve early detection of changes in network conditions [2][3][4]. An AE is a kind of neural network capable of unsupervised learning of the intrinsic complex structure of data and is currently drawing a great deal of interest for anomaly detection applications.…”

Section: Network Anomaly Detection Technologymentioning

confidence: 99%

Creating New Value by Leveraging Network-AI Technology in Service Operations

Watanabe,

Ikeda,

Nakano

et al. 2018

NTT Technical Review

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“…Recently, the use of artificial intelligence (AI) in network operations has attracted attention [1]. Although research is progressing in areas such as anomaly detection, root cause analysis, and control recovery, research on estimating failure scales is limited thus far.…”

Section: Introductionmentioning

confidence: 99%

Method for Network-Anomaly Detection and Failure-Scale Estimation

Ogawa,

Kawahara

2024

IEICE Commun. Express

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In this study, we propose a novel method for network-anomaly detection and failure-scale estimation using autoencoders, which are a type of neural network. The proposed method first divides the network into several groups. Subsequently, anomalies are detected using an autoencoder for each intergroup traffic, and the failure-scale is estimated from the number of autoencoders that have detected anomalies. We experimentally investigated anomaly detection during communication through a virtual network built using the network emulator Mininet and confirmed that the proposed method can successfully detect anomalies and estimate the failure scale.

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Application of AI to Network Operation

Cited by 5 publications

References 25 publications

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey

Machine Learning-Based Anomaly Detection in NFV: A Comprehensive Survey

Creating New Value by Leveraging Network-AI Technology in Service Operations

Method for Network-Anomaly Detection and Failure-Scale Estimation

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