Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

Lu, Yutao; Wang, Juan; Liu, Miao; Zhang, Kaixuan; Gui, Guan; Ohtsuki, Tomoaki; Adachi, Fumiyuki

doi:10.1109/tvt.2020.2995160

Cited by 18 publications

(4 citation statements)

References 50 publications

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“…Deep learning applications in mobile networks are broad; these applications range from monitoring, quality improvement, management and design, among others. One of the applications that has been studied recently is the use of semi-supervised machine learning to detect anomalies in cellular networks [48]. AI-based computer vision has also been studied using DL in 6G wireless networks, For artificial intelligence, the different techniques of this technology are focused on improving the management of mobile networks by defining characteristics for 5G network software with efficient, agile and mainly autonomous and cognitive management [34].…”

Section: Resultsmentioning

confidence: 99%

“…Deep learning applications in mobile networks are broad; these applications range from monitoring, quality improvement, management and design, among others. One of the applications that has been studied recently is the use of semi-supervised machine learning to detect anomalies in cellular networks [48]. AI-based computer vision has also been studied using DL in 6G wireless networks, which use DL algorithms to solve different problems when recognizing images and objects via AI in 6G networks [49].…”

Section: Resultsmentioning

confidence: 99%

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Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

García-Pineda,

Valencia-Arias,

Patiño-Vanegas

et al. 2023

Informatics

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This article aims to examine the research trends in the development of mobile networks from machine learning. The methodological approach starts from an analysis of 260 academic documents selected from the Scopus and Web of Science databases and is based on the parameters of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Quantity, quality and structure indicators are calculated in order to contextualize the documents’ thematic evolution. The results reveal that, in relation to the publications by country, the United States and China, who are competing for fifth generation (5G) network coverage and are responsible for manufacturing devices for mobile networks, stand out. Most of the research on the subject focuses on the optimization of resources and traffic to guarantee the best management and availability of a network due to the high demand for resources and greater amount of traffic generated by the many Internet of Things (IoT) devices that are being developed for the market. It is concluded that thematic trends focus on generating algorithms for recognizing and learning the data in the network and on trained models that draw from the available data to improve the experience of connecting to mobile networks.

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Section: Resultsmentioning

confidence: 99%

“…Deep learning applications in mobile networks are broad; these applications range from monitoring, quality improvement, management and design, among others. One of the applications that has been studied recently is the use of semi-supervised machine learning to detect anomalies in cellular networks [48]. AI-based computer vision has also been studied using DL in 6G wireless networks, which use DL algorithms to solve different problems when recognizing images and objects via AI in 6G networks [49].…”

Section: Resultsmentioning

confidence: 99%

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

García-Pineda,

Valencia-Arias,

Patiño-Vanegas

et al. 2023

Informatics

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show abstract

“…Wireless communication networks, including personal area networks, local area networks, metropolitan area networks, wide area networks, and virtual private networks, rely on wireless data connections between mobile network nodes [42]. However, the shared wireless channel raises privacy concerns and necessitates robust network security [43].…”

Section: ) ML For Ad In Wireless Communication Networkmentioning

confidence: 99%

Qualitative Survey on Artificial Intelligence Integrated Blockchain Approach for 6G and Beyond

Pathak,

Pandya,

Bhatia

et al. 2023

IEEE Access

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Utilizing the 0.1 to 10 THz spectrum in the next-generation wireless communication networks holds potential for futuristic applications. However, managing resources to accommodate numerous devices raises privacy and security concerns. Further, technology proliferation entwines devices, infrastructure complexity, and resources. Indeed, the transition from 5G (fifth-generation) to 6G (sixth-generation) signifies a progression towards high-speed data rates, minimal latency, and seamless integration of artificial intelligence, enabling ground-breaking applications and services. However, it complicates network management, privacy, resource allocation, and data processing. Notably, integrating Blockchain Technology (BCT) and Machine Learning (ML) is a promising solution, enhancing security, decentralization, trust in ML decisions, and efficient data sharing. This survey thoroughly reviews the integrated ML and BCT, showcasing their collaborative enhancement of network security, decentralization, trust in ML decisions, immutability, and efficient model sharing. Furthermore, we also delve into various distinctive topics, such as BCT-enabled spectrum refarming, rate splitting multiple access, 6G radar-based communication, reconfigurable intelligent surfaces, visible light communication, and integrated sensing and communication. Moreover, it also explores the integration of ML and BCT in novel 6G communication technologies, including molecular, holographic, and semantic communication. Finally, critical open issues, challenges, solutions, and futuristic scope are identified for forthcoming researchers. INDEX TERMS Terahertz (THz) Communication, Artificial Intelligence (AI), Machine Learning (ML), Blockchain Technology (BCT), and Resource Allocation (RA).

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“…Additionally, the LSTM network can model a longer data sequence, applying cell states and gated outputs to retain useful information, and let go of unnecessary ones [ 19 ]. Such application is often extended to analyze intrusion detection systems or anomaly detection in network traffic [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection

Oluwasanmi

Aftab

Baagyere

et al. 2021

Sensors

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Today, accurate and automated abnormality diagnosis and identification have become of paramount importance as they are involved in many critical and life-saving scenarios. To accomplish such frontiers, we propose three artificial intelligence models through the application of deep learning algorithms to analyze and detect anomalies in human heartbeat signals. The three proposed models include an attention autoencoder that maps input data to a lower-dimensional latent representation with maximum feature retention, and a reconstruction decoder with minimum remodeling loss. The autoencoder has an embedded attention module at the bottleneck to learn the salient activations of the encoded distribution. Additionally, a variational autoencoder (VAE) and a long short-term memory (LSTM) network is designed to learn the Gaussian distribution of the generative reconstruction and time-series sequential data analysis. The three proposed models displayed outstanding ability to detect anomalies on the evaluated five thousand electrocardiogram (ECG5000) signals with 99% accuracy and 99.3% precision score in detecting healthy heartbeats from patients with severe congestive heart failure.

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Semi-Supervised Machine Learning Aided Anomaly Detection Method in Cellular Networks

Cited by 18 publications

References 50 publications

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

Research Trends in the Use of Machine Learning Applied in Mobile Networks: A Bibliometric Approach and Research Agenda

Qualitative Survey on Artificial Intelligence Integrated Blockchain Approach for 6G and Beyond

Attention Autoencoder for Generative Latent Representational Learning in Anomaly Detection

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