Public cloud networks oriented deep neural networks for effective intrusion detection in online music education

Zhang, Jianan; Peter, J Dinesh; Shankar, Achyut; Viriyasitavat, Wattana

doi:10.1016/j.compeleceng.2024.109095

Cited by 9 publications

(1 citation statement)

References 27 publications

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“…The article [100] discussed applying intrusion detection in online music education using Deep Neural Networks (DNN) on public cloud networks. The research aimed to tackle the challenges of detecting intrusions in this domain.…”

Section: B Ids Anomaly Based On DL -Existing Research Workmentioning

confidence: 99%

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

Abdallah,

Saif Rashed Obaid Alkaabi,

Bark Nasser Douman Alameri

et al. 2024

IEEE Access

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In the rapidly evolving landscape of computing and networking, the concepts of cloud networks have gained significant prominence. Although the cloud network offers on-demand access to shared resources, anomalies pose potential risks to the integrity and security of cloud networks. However, protecting the cloud network against anomalies remains a challenge. Unlike traditional detection techniques, machine learning (ML) and deep learning (DL) offer new and adaptable methods for detecting anomalies in cloud networks. The objective of this study is to comprehensively explore existing ML /DL methods for detecting different anomalies based on distributed denial of service anomaly (DDoS) and intrusion detection systems (IDS) in cloud networks. The study seeks to address the gaps in anomaly detection for cloud networks, proposing potential solutions for anomaly detection in these cloud environments. The ultimate goal is to contribute valuable insights and practical solutions to enhance the security and reliability of cloud networks through effective anomaly detection by ML/ DL techniques. Methodologies for ML/DL are explained, along with their advantages, disadvantages, and respective approaches. In addition, a summary of the comparison between different ML/ DL models is also included.

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Section: B Ids Anomaly Based On DL -Existing Research Workmentioning

confidence: 99%

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

Abdallah,

Saif Rashed Obaid Alkaabi,

Bark Nasser Douman Alameri

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Impact of Music Teaching on Student Mental Health Using IoT, Recurrent Neural Networks, and Big Data Analytics

Jia

2024

Mobile Netw Appl

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A multifaceted architecture to Automate Essay Scoring for assessing english article writing: Integrating semantic, thematic, and linguistic representations

Wang

2024

Computers and Electrical Engineering

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Public cloud networks oriented deep neural networks for effective intrusion detection in online music education

Cited by 9 publications

References 27 publications

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques— Recent Research Advancements

Impact of Music Teaching on Student Mental Health Using IoT, Recurrent Neural Networks, and Big Data Analytics

A multifaceted architecture to Automate Essay Scoring for assessing english article writing: Integrating semantic, thematic, and linguistic representations

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