Hybrid semantic deep learning architecture and optimal advanced encryption standard key management scheme for secure cloud storage and intrusion detection

Prabhakaran, V. T.; Kulandasamy, Ashokkumar

doi:10.1007/s00521-021-06085-5

Cited by 35 publications

(20 citation statements)

References 31 publications

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“…Prabhakaran and Kulandasamy 36 have presented hybrid semantic deep learning (HSDL) architecture by incorporating long short‐term memory, support vector machine, convolutional neural network. Using a semantic layer called Word2Vec embedding layer, the semantic data included in the network traffic was found.…”

Section: Literature Surveymentioning

confidence: 99%

Cycle‐consistentgenerative adversarial network optimized with water strider optimization algorithm fostered intrusion detection framework for securing cloud computing environment

Preethi

Sugitha²,

Kavitha³

2022

Concurrency and Computation

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Cloud computing is the delivery of computing services including servers, storage, databases, networking, software, analytics, and intelligence over the internet ("the cloud") to offer faster innovation, flexible resources, and economies of scale. In this article, cycle-consistent generative adversarial network (CCGAN) optimized with water strider optimization (WSO) algorithm fostered intrusion detection system (IDS) is proposed to secure the cloud computing (CC) environment (IDS-CC-CCGAN-WSOA).Initially, the input data's are gathered via NSL-KDD benchmark dataset. Then it is given to preprocessing. During preprocessing, it debugs the redundancy and missing value is restored using local least squares. Then, the preprocessed output is fed to the feature selection level. The optimum features are compiled utilizing correlation feature selection approach. This optimum features based, the data's are categorized as normal and anomalous data. The weights of this network are optimized by water strider optimization to attain effectual and optimum solution for recognizing the intruders. The proposed approach is executed in MATLAB. The performance metrics is examined to validate the performance of the proposed approach. Finally, the proposed approach attains 13.9367%, 13.268%, and 13.739% higher accuracy analyzed to the existing approaches, such as IDS-CC-DBN-CSSA, IDS-CC-DNN-IGASAA, and IDS-CC-MLPNN-ABC.

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Section: Literature Surveymentioning

confidence: 99%

Cycle‐consistentgenerative adversarial network optimized with water strider optimization algorithm fostered intrusion detection framework for securing cloud computing environment

Preethi

Sugitha²,

Kavitha³

2022

Concurrency and Computation

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“…The new conceptions presented using the clouds, like computational outsourcing, sharing of resources and external data warehouse, maximize in security and privacy concern are created new security challenges. Also, huge scale clouds, the proliferation of mobile access devices (smart phones, tablets), direct access to cloud infrastructure amplifies the cloud vulnerabilities and threats 24 . Nowadays, clouds become more popular and their security concern grows bigger, because they become more attractive attack for the targets and the attention of digital assets.…”

Section: Introductionmentioning

confidence: 99%

“…Also, huge scale clouds, the proliferation of mobile access devices (smart phones, tablets), direct access to cloud infrastructure amplifies the cloud vulnerabilities and threats. 24 Nowadays, clouds become more popular and their security concern grows bigger, because they become more attractive attack for the targets and the attention of digital assets. An ID plays a major role in the cloud security, because it identifies the unknown and known attacks efficiently.…”

Section: Introductionmentioning

confidence: 99%

Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

Devi

Muthusenthil

2022

Trans Emerging Tel Tech

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Cloud computing (CC) is vulnerable for the attacks of current information technology, because it prolongs and uses the information technology infrastructure, traditional operating system, and applications. Several security issues in detecting irregular network performances are confronted by the CC environment. With the intention of solving these security issues, dual-channel capsule generation adversarial network optimized with red fox optimization algorithm fostered intrusion detection framework (IDS-CC-DCCGAN-RFOA) is proposed in this article, for securing the privacy attacks in CC environment. Initially, the data are taken from NSL-KDD benchmark dataset. Then the data are given to the preprocessing segment. When using the developed random forest and local least squares in preprocessing segment, the redundancy, and missing values are purged. Then the output of preprocessing is given to the feature selection stage. In feature selection, the optimal features are extracted using univariate ensemble feature selection (UEFS) technique. Based on the optimal features, the data are classified into secured data and privacy attack data based on dual-channel capsule generative adversarial network. Here, the weight of dual-channel capsule generative adversarial network is optimally tuned using the red fox optimization algorithm for generating an effective and optimal solution to detect the intruders. The proposed IDS-CC-DCCGAN-RFOA method is implemented in MATLAB. Here, the performance metrics, like accuracy, specificity, sensitivity, F-score, precision, false positive rate, AUC are analyzed.Then, the proposed IDS-CC-CCGAN-WSOA method provides 13.9367%, 11.12%, 13.268%, and 13.739% higher accuracy and 24.54%, 29.76%, 12.87%, and 34.21% higher sensitivity compared with the existing methods, like intrusion detection framework in cloud computing using deep belief network with salp swarm algorithm (IDS-CC-DBN-CSSA), intrusion detection framework in cloud computing using deep neural network with improved genetic algorithm and simulated annealing algorithm (IDS-CC-DNN-IGASAA), intrusion detection for cloud computing using multilayer perceptron neural network and artificial bee colony optimization algorithm (IDS-CC-MLPNN-ABC), and intrusion detection for cloud computing using recurrent convolutional neural network with ant lion optimization algorithm (IDS-CC-RCNN-ALO).

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“…The concept of utilizing different machine learning (ML) in Neuro-Cryptography is growing rapidly. Various researchers have proposed many Neuro-Cryptography rules [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Instant-Hybrid Neural-Cryptography (IHNC) based on fast machine learning

Badr

2022

Neural Comput & Applic

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Nowadays, cryptographic systems’ designers are facing significant challenges in their designs. They have to constantly search for new ideas of fast unbreakable algorithms with a very powerful key generator. In this paper, we propose a novel hybrid neural-cryptography methodology. It depends on new rule of very fast Backpropagation (BP) instant machine learning (ML). This proposed Hybrid Cryptography system is constructed from Encryptor and Decryptor based on the asymmetric Autoencoder type. The Encryptor encrypts and compresses a set of data to be instant code (i-code) using public key. While the Decryptor recovers this i-code (ciphered-data) based on two keys together. The first is the private key and the other is called instant-key (i-key). This i-key is generated from 3 factors as well (the original data itself, the generated i-code and the private key). The i-key is changing periodically with every transformation of plain data set, so it is powerful unpredictable key against the brute force.

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Hybrid semantic deep learning architecture and optimal advanced encryption standard key management scheme for secure cloud storage and intrusion detection

Cited by 35 publications

References 31 publications

Cycle‐consistentgenerative adversarial network optimized with water strider optimization algorithm fostered intrusion detection framework for securing cloud computing environment

Cycle‐consistentgenerative adversarial network optimized with water strider optimization algorithm fostered intrusion detection framework for securing cloud computing environment

Intrusion detection framework for securing privacy attack in cloud computing environment using DCCGAN‐RFOA

Instant-Hybrid Neural-Cryptography (IHNC) based on fast machine learning

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