Design of network security storage system based on under cloud computing technology

ThiBac, Do; Minh, Nguyen Hieu

doi:10.1016/j.compeleceng.2022.108334

Cited by 4 publications

(1 citation statement)

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“…Utilizing the CICDDoS 2019 dataset for performance evaluation, the robust BPNN-based detection methodology achieved high accuracy in detecting reflective DDoS attacks, demonstrating its effectiveness in containerized cloud-native settings with noteworthy runtime efficiency. ThiBac and Hieu′s 2022 research [17] explores advancements in information storage security through a distributed model where data is fragmented and stored across multiple providers. This construction significantly enhances flexibility, security, and quality of service by reducing reliance on a single storage provider, showcasing a forward-thinking approach to data storage in cloud environments.…”

Section: Related Workmentioning

confidence: 99%

Improved restricted Boltzmann machine-based optimization model for the network security system in cloud environment

2024

Eng. Res. Express

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This paper introduces a novel approach to enhancing computer network security through deep learning and cloud technologies, focusing on combating insider DDoS attacks in cloud computing. By employing the IRBM architecture and a trust-oriented secure cloud framework, the work utilizes parameter tuning in RBM through the Gradient-Based Optimization (GBO) algorithm resulting in the proposed IRBM-GBO model. This model significantly outperforms SVM, SC-CNSP-ML, and RBM in terms of latency and Packet Delivery Ratio (PDR), showing 21.67%, 14.55%, and 9.62% improvements in latency, and 10.23%, 5.43%, and 12.79% in PDR, respectively. The research highlights the potential of combining soft computing with deep learning and cloud technologies to identify cyberattacks by emphasising improvements in latency, Quality of Service (QoS), throughput, and PDR across various cybersecurity datasets.

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Section: Related Workmentioning

confidence: 99%