Steganography Adaptation Model for Data Security Enhancement in Ad-Hoc Cloud Based V-BOINC Through Deep Learning

Mawgoud, Ahmed A.; Taha, Mohamed Hamed N.; Kotb, Amira

doi:10.1007/978-3-031-03918-8_7

Cited by 3 publications

(4 citation statements)

References 8 publications

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“…Numerous industries, including healthcare, use machine learning to anticipate diseases including COVID19, osteoporosis, and schistosomiasis.Regarding security evaluation, the performance of recognition and detection is still a problem, as seen with DDoS attacks and blockchain. Numerous research have employed classification algorithms with the goal of identifying and resolving the DDoS attack.DDoS assaults are simple to carry out by making use of network vulnerabilities and software service demands [6].…”

Section: Introductionmentioning

confidence: 99%

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Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

Thomas

Gupta

Rajan

et al. 2023

Preprint

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Artificial neural networks (ANN), fuzzy inference systems, approximate reasoning, and derivative-free optimization approaches like evolutionary computation, among others, are all components of Soft Computing (SC), an innovative method for building computationally intelligent methods. Due to its widespread use and benefits, cloud computing is currently a major focus for researchers. The distributed nature of cloud computing and its complete reliance on the internet for service provision present security challenges, the most serious of which is insider Distributed Denial of Service (DDoS), which results in complete deactivation of services. Based on machine learning and the cloud, this study proposes a novel method for improving computing network security. Using a trust-based secure cloud environment and the Kernel principal component encoder architecture, the network monitoring is carried out. The soft computing environment-based detection of a cyberattack on the network is the purpose for the security enhancement. Throughput, QoS, latency, and packet delivery ratio for various monitored cyber security datasets are the focus of the experimental analysis.the proposed technique attainednetwork security analysis of 89%, throughput of 98%, QoS of 66%, latencyof 59%, packet delivery ratio of 83%.

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Section: Introductionmentioning

confidence: 99%

“…There are five distinct classes in this data set: Normal, DoS, R2L, U2R, and Probes. There are 5,092 and 6,890 records for the training and test, respectively[6]. Our initial research goal was to eliminate as many variables as possible.…”

mentioning

confidence: 99%

Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

Thomas

Gupta

Rajan

et al. 2023

Preprint

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“… The end-user implies a consistent level of trust towards the volunteer resources and grid systems; as there is no existence for a trust relation between the infrastructure system and the end user. Business continuity can be provided through a group of unreliable nodes, the ad-hoc cloud system provides the availability for both host and guest users within the ad-hoc cloud system in case any failure case occurred periodically [ 7 ]. The operated host processes –regardless the resources’ consumption level- were not affected by any means with the ad-hoc cloud system over the passing time.…”

Section: Introductionmentioning

confidence: 99%

“…Business continuity can be provided through a group of unreliable nodes, the ad-hoc cloud system provides the availability for both host and guest users within the ad-hoc cloud system in case any failure case occurred periodically [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

et al. 2022

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In the early days of digital transformation, the automation, scalability, and availability of cloud computing made a big difference for business. Nonetheless, significant concerns have been raised regarding the security and privacy levels that cloud systems can provide, as enterprises have accelerated their cloud migration journeys in an effort to provide a remote working environment for their employees, primarily in light of the COVID-19 outbreak. The goal of this study is to come up with a way to improve steganography in ad hoc cloud systems by using deep learning. This research implementation is separated into two sections. In Phase 1, the “Ad-hoc Cloud System” idea and deployment plan were set up with the help of V-BOINC. In Phase 2, a modified form of steganography and deep learning were used to study the security of data transmission in ad-hoc cloud networks. In the majority of prior studies, attempts to employ deep learning models to augment or replace data-hiding systems did not achieve a high success rate. The implemented model inserts data images through colored images in the developed ad hoc cloud system. A systematic steganography model conceals from statistics lower message detection rates. Additionally, it may be necessary to incorporate small images beneath huge cover images. The implemented ad-hoc system outperformed Amazon AC2 in terms of performance, while the execution of the proposed deep steganography approach gave a high rate of evaluation for concealing both data and images when evaluated against several attacks in an ad-hoc cloud system environment.

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Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

et al. 2023

View full text Add to dashboard Cite

Steganography Adaptation Model for Data Security Enhancement in Ad-Hoc Cloud Based V-BOINC Through Deep Learning

Cited by 3 publications

References 8 publications

Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

A deep learning based steganography integration framework for ad-hoc cloud computing data security augmentation using the V-BOINC system

Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment

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