RETRACTED ARTICLE: A novel centralized cloud information accountability integrity with ensemble neural network based attack detection approach for cloud data

Punitha, A.; Indumathi, G.

doi:10.1007/s12652-020-01916-0

Cited by 9 publications

(5 citation statements)

References 30 publications

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“…In other words, all inclusive experimentation and trustworthy results need joint consideration. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] emphases only on basic attacks where data is processed with low security features. Even many methods does not incorporate learning techniques for avoiding attacks from external users.…”

Section: Research Gap and Motivationmentioning

confidence: 99%

“…IDS are disseminated IDS because it use hypervisors to identify network hosts and disseminate the results. To investigate DDoS attacks in the cloud, machine learning is used to the NSL-KDD dataset [5]. The attributes chosen by both LVQ and PCA attribute selection approaches are essential for a successful implementation of mining algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Then, the multilayer neural network was used to distinguish harmful activities. According to Punitha and Indumathi (2020) [5], entrusting our data security to a central cloud database, which uses an algorithm that generates the empire's own security keys, puts our data security at risk. The suggested system is also capable of detecting and monitoring how information is used.…”

Section: Introductionmentioning

confidence: 99%

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Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

Kshirsagar

Manoharan²,

Alterazi

et al. 2022

Journal of Sensors

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Cloud services are a popular concept used to describe how internet-based services are delivered and maintained. The computer technology environment is being restructured with respect to information preservation. Data protection is of critical importance when storing huge volumes of information. In today’s cyber world, an intrusion is a significant security problem. Services, information, and services are all vulnerable to attack in the cloud due to its distributed structure of the cloud. Inappropriate behavior in the connection and in the host is detected using intrusion detection systems (IDS) in the cloud. DDoS attacks are difficult to protect against since they produce massive volumes of harmful information on the network. This assault forces the cloud services to become unavailable to target consumers, which depletes computer resources and leaves the provider exposed to massive financial and reputational losses. Cyber-analyst data mining techniques may assist in intrusion detection. Machine learning techniques are used to create many strategies. Attribute selection techniques are also vital in keeping the dataset’s dimensionality low. In this study, one method is provided, and the dataset is taken from the NSL-KDD dataset. In the first strategy, a filtering method called learning vector quantization (LVQ) is used, and in the second strategy, a dimensionality-simplifying method called PCA. The selected attributes from each technique are used for categorization before being tested against a DoS attack. This recent study shows that an LVQ-based SVM performs better than the competition in detecting threats.

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Section: Research Gap and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

Kshirsagar

Manoharan²,

Alterazi

et al. 2022

Journal of Sensors

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show abstract

“…The residual network used the residual block that allows the forward pass via a network is given in Equation (32).…”

Section: 46mentioning

confidence: 99%

“…Cloud infrastructure includes many physical servers, which host many services and then run a number of virtual instances and manage cloud services 32 . The number of physical servers can be denoted as

Hi

, where,

Hi,i=1,2,\dots, n

.…”

Section: Proposed Ddos Attack Detection In Cloud Using Ensemble Model...mentioning

confidence: 99%

DDoS attack detection in cloud using ensemble model tuned with optimal hyperparameter

Reddy,

Meera

2024

Adaptive Control & Signal

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SummaryDDoS attacks are a type of cloud incursion that lessen service degradation. DDoS attacks target the cloud network with invalid requests, rejecting legitimate requests. Such attacks disrupt the entire cloud architecture, thus it needs efficient detection methods to spot their presence. This study proposes a novel ensemble classification model for DDoS incursion detection. Pre‐processing, feature extraction, and attack detection are the three main components of the suggested intrusion detection system. Improved data imbalance processing is processed during pre‐processing. Features including HOS‐based features enhanced entropy‐based features, correlation features, and raw features are extracted from the pre‐processed data. The generated features are trained using an ensemble model, which integrates classifiers like SVM, RF, NN, LSTM, and DRN, during the attack detection phase. A new hybrid approach known as TUDMA optimally trains the model by setting the ideal weight because DRN provides the final detected outcome. By reducing errors, this ideal training will guarantee an improvement in detection results. The suggested hybrid optimization combines the two techniques, TDO and DMO. The accuracy of the methods FS‐WOA‐DNN, RBF‐PSO, SMA, BRO, SLO, and NMRA was 86.73%, 85.69%, 84.58%, 87.43%, 88.91%, and 89.78%, respectively, while the accuracy of the TUDMA was 95.34% in the 80th learning percentage. Finally, for many measurements, the suggested efficiency is superior to the traditional methods.

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In Cloud Computing Detection of DDoS Attack Using AI-Based Ensembled Techniques

Shrivastava

Gautam

2023

Advances in Cognitive Science and Communications

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RETRACTED ARTICLE: A novel centralized cloud information accountability integrity with ensemble neural network based attack detection approach for cloud data

Cited by 9 publications

References 30 publications

Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

Construal Attacks on Wireless Data Storage Applications and Unraveling Using Machine Learning Algorithm

DDoS attack detection in cloud using ensemble model tuned with optimal hyperparameter

In Cloud Computing Detection of DDoS Attack Using AI-Based Ensembled Techniques

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