On Artificial Neural Network used in Cloud Computing Security - A Survey

Nita, Stefania Loredana; Mihăilescu, Marius Iulian

doi:10.1109/ecai.2018.8679086

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…For analysis, the sample areas of 400 × 400 mm 2 with a distance of 10 µm between adjacent profiles, at a scanning rate of 100 µm/s, and an applied mass of 0.5 mg, were used. The results were analyzed via APEX 3D BASIC V7USA, (Milpitas, CA, USA) dedicated software and the density of square unit per surface area was calculated using the Abbott-Firestone algorithm, a cumulative probability density function of the total measured heights [32][33][34].…”

Section: Substrates Topography Characterizationmentioning

confidence: 99%

Influence of Laser-Designed Microstructure Density on Interface Characteristics and on Preliminary Responses of Epithelial Cells

et al. 2020

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Current trends in designing medical and tissue engineering systems rely on the incorporation of micro- and nano-topographies for inducing a specific cellular response within the context of an aimed application. As such, dedicated studies have recently focused on understanding the possible effects of high and low density packed topographies on the behavior of epithelial cells, especially when considering their long-term viability and functionality. We proposed to use stair-like designed topographies with three different degrees of distribution, all created in polydimethylsiloxane (PDMS) as active means to monitor cell behavior. Our model cellular system was human bronchial epithelial cells (BEAS-2B), a reference line in the quality control of mesenchymal stem cells (MSCs). PDMS microtextured substrates of 4 µm square unit topographies were created using a mold design implemented by a KrF Excimer laser. Varying the spacing between surface features and their multiscale level distribution led to irregular stairs/lines in low, medium and high densities, respectively. Profilometry, scanning electron and atomic force microscopy, contact angle and surface energy measurements were performed to evaluate the topographical and interface characteristics of the designed surfaces, while density-induced cellular effects were investigated using traditional cell-based assays. Our analysis showed that microstructure topographical distribution influences the adhesion profiles of epithelial cells. Our analysis hint that epithelial organoid formation might be initiated by the restriction of cell spreading and migration when using user-designed, controlled micro-topographies on engineered surfaces.

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Section: Substrates Topography Characterizationmentioning

confidence: 99%

Influence of Laser-Designed Microstructure Density on Interface Characteristics and on Preliminary Responses of Epithelial Cells

et al. 2020

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“…Still, it faces the major problems of increased time consumption, high computational complexity, and slow processing at the time of key generation and authentication. Also, the classification methodologies [19][20][21] limit the issues of high error rate, inefficient prediction results, and delay in the process, which degrades the performance of the entire security system. In order to solve these problems, the proposed work is aimed at developing an intelligent security scheme based on AI technology for healthcare systems.…”

Section: Introductionmentioning

confidence: 99%

Healthcare Data Security Using IoT Sensors Based on Random Hashing Mechanism

et al. 2022

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Providing security to the healthcare data stored in an IoT-cloud environment is one of the most challenging and demanding tasks in recent days. Because the IoT-cloud framework is constructed with an enormous number of sensors that are used to generate a massive amount of data, however, it is more susceptible to vulnerabilities and attacks, which degrades the security level of the network by performing malicious activities. Hence, Artificial Intelligence (AI) technology is the most suitable option for healthcare applications because it provides the best solution for improving the security and reliability of data. Due to this fact, various AI-based security mechanisms are implemented in the conventional works for the IoT-cloud framework. However, it faces significant problems of increased complexity in algorithm design, inefficient data handling, not being suitable for processing the unstructured data, increased cost of IoT sensors, and more time consumption. Therefore, this paper proposed an AI-based intelligent feature learning mechanism named Probabilistic Super Learning- (PSL-) Random Hashing (RH) for improving the security of healthcare data stored in IoT-cloud. Also, this paper is aimed at reducing the cost of IoT sensors by implementing the proposed learning model. Here, the training model has been maintained for detecting the attacks at the initial stage, where the properties of the reported attack are updated for learning the characteristics of attacks. In addition to that, the random key is generated based on the hash value of the data matrix, which is incorporated with the standard Elliptic Curve Cryptography (ECC) technique for data security. Then, the enhanced ECC-RH mechanism performs the data encryption and decryption processes with the generated random hash key. During performance evaluation, the results of both existing and proposed techniques are validated and compared using different performance indicators.

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“…Cloud adopts all security problems of its modules because of its complex framework. ANN are a new technique in cloud that is applied mainly in the detection of intrusion in security [1]. Machine learning is a subdivision of artificial intelligence.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Security in Cloud Computing Using Neural Network and Encryption

Sana

Javaid

et al. 2021

IEEE Access

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In the last five years, demand for cloud computing among businesses and individual users is increasing immensely because of numerous reasons including, improved productivity, efficiency and speed, cost savings, performance, and most importantly security. Machine learning techniques are making progress in a variety of domains of cloud computing to resolve security concerns and manage data efficiently. In cloud security, a relatively novel approach is Artificial Neural Networks (ANN). We propose a new security design using neural network and encryption to confirm a safe communication system in the cloud environment, by letting the third parties access the information in an encrypted form without disclosing the data of the provider party to secure important information. We recommend a solution based on fully homomorphic encryption (FHE) to handle sensitive information without revealing the original data. The encryption technique we considered is matrix operation-based randomization and encipherment (MORE), which allows the computations to be performed directly on floating-point data within a neural network with a minor computational overhead. In this paper, we examined the speech and voice recognition problem and the performance of the proposed method has been validated in MATLAB simulation. Results showed that applying neural network training with MORE improved accuracy, runtime, and performance. These results highlight the potential of the proposed neural network and encryption technique to protect the privacy and providing high accuracy in a reasonable period when compared to other state of the art techniques.

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On Artificial Neural Network used in Cloud Computing Security - A Survey

Cited by 9 publications

References 18 publications

Influence of Laser-Designed Microstructure Density on Interface Characteristics and on Preliminary Responses of Epithelial Cells

Influence of Laser-Designed Microstructure Density on Interface Characteristics and on Preliminary Responses of Epithelial Cells

Healthcare Data Security Using IoT Sensors Based on Random Hashing Mechanism

Enhanced Security in Cloud Computing Using Neural Network and Encryption

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